
import vcf
import allel 
import vcftoolz
import pysam
import vcfpy
import sys
import os
from pathlib import Path
import pandas as pd


from platform import python_version

print(python_version())

3.5.2


import pandas as pd
data = pd.read_csv('new_core.txt', sep='\t',header=None)
data.shape

(70604, 875)


import pandas as pd 
import glob

# SNP files for each strain
dirname = './output/*/*.csv'

# synonymous position/variants which need tp be removed 
position_of_interest = set()

for fname in glob.glob(dirname):
    vcf_names = pd.read_csv(fname)
    vcf_names = vcf_names[vcf_names['EFFECT'].astype(str).str.contains("synonymous_variant", na=False)]
    position_of_interest.update(vcf_names['POS'])


# total number of synonymous variants
len(position_of_interest)

396270


data = data[~data[1].isin(position_of_interest)]


# shape of the dataset after synonymous variant removal 
data.shape

(29591, 867)


# Meta data properties form the dataset
vcf_phenotype = pd.read_csv('./disease_strains.tsv',sep='\t')


# Metadata column names 
vcf_phenotype.columns

Index(['assembly_id', 'Uberstrain', 'Name',
       'Data Source(Accession No.;Sequencing Platform;Sequencing Library;Insert Size;Experiment;Status)',
       'Source Niche', 'Source Type', 'Source Details', 'Host Ethnicity',
       'Host Sex', 'Host Age', 'Collection Year', 'Collection Month',
       'Collection Day', 'Collection Time', 'Continent', 'Country', 'Region',
       'District', 'City', 'Post Code', 'Latitude', 'Longitude', 'Species',
       'Species Purity', 'Uploader', 'Comment', 'Bio Project ID', 'Project',
       'Sample', 'Secondary Sample', 'Date Entered', 'Release Date', 'Barcode',
       'Citations', 'Contact', 'Disease', 'Alias', 'Antimicrobial Resistance',
       'Status', 'Version', 'Length', 'Species2', 'Species2 Purity',
       'Contig Number', 'No. N's', 'Barcode2', 'Disease_gastritis',
       'Disease_atrophy', 'Disease_chronic_gastritis', 'Disease_ulcer',
       'Disease_cancer', 'Disease_metaplasia', 'Disease_dysplasia',
       'Disease_malt', 'Disease_location_duodenum', 'Disease_location_stomach',
       'assembly_id.1', 'original_assembly_filename', 'assembly_filename',
       'GWAS group', 'core_gene_hits', 'percent_core', 'n_contigs', 'sum_len',
       'min_len', 'avg_len', 'max_len', 'Q1', 'Q2', 'Q3', 'sum_gap', 'N50',
       'Q20(%)', 'Q30(%)', 'n_proteins', 'n_cags', 'n_split_cags',
       'Geographic provenance', 'FineStructure Population', 'Source'],
      dtype='object')


# Phoenotypes in teh dataset 
print(set(vcf_phenotype['GWAS group'].astype(str)))

{'nan', 'Non Atrophic Gastritis', 'Progressive towards Cancer', 'Gastric Cancer'}


# Removing "nan" phenotypes
vcf_phenotype_final = vcf_phenotype[["assembly_id", "Uberstrain","Name","GWAS group"]]


# Retrieving file with correct order of strain names in the core_vcf file 
vcf_chrom = list(pd.read_csv('./new_order1.txt',sep='\t'))[9:]
vcf_chrom[:10]

['2003-103',
 '2004-20',
 '2005-100',
 '2005-126',
 '2005-98',
 '2006-4',
 '2006-407',
 '2006-479',
 '2006-480',
 '2006-52']


# Making a dataframe with phenotypes and strain name 
new_df_vcf_phen = pd.DataFrame()


for i in range(len(vcf_chrom)):
    new =  vcf_phenotype_final[vcf_phenotype_final['assembly_id']==vcf_chrom[i]]
    new_df_vcf_phen = pd.concat([new_df_vcf_phen, new], axis=0)

# Wrangling dataset     
new_df_vcf_phen = new_df_vcf_phen.reset_index()
del new_df_vcf_phen['index']


# 
new_df_vcf_phen


#Represneting SNP names 
data["POSITION"]=data[1].astype(str)+"."+data[3].astype(str)+"/"+data[4]


data


#Deleting irrelavent columns
del data[0]
del data[1]
del data[2]
del data[3]
del data[4]
del data[5]
del data[6]
del data[7]
del data[8]


# Transposing  data for SNP's as columns names
dataT = data.T


dataT = dataT.reset_index()
del dataT['index']


dataT


# Removing unwanted rows and making it as the column id
header_row = 866
dataT.columns = dataT.iloc[header_row]
dataT = dataT.drop(866,axis=0)


dataT


dataT['Phenotype'] = new_df_vcf_phen['GWAS group']


# Shape of the transformed data
dataT.shape

(866, 29592)


dataT.iloc[:,:-1]


# Removing "nan" phenotypes
dataTN = dataT[~dataT['Phenotype'].isnull()]


dataTN['Phenotype'].value_counts()

Non Atrophic Gastritis        416
Progressive towards Cancer    202
Gastric Cancer                 96
Name: Phenotype, dtype: int64


dataTN


# Reindexing after de
dataTN = dataTN.reset_index()
del dataTN['index']


GSxNAG = dataTN[dataTN['Phenotype']!="Progressive towards Cancer"]

set(GSxNAG['Phenotype'])

GSxPTC = dataTN[dataTN['Phenotype']!="Non Atrophic Gastritis"]

NAGxPTC = dataTN[dataTN['Phenotype']!="Gastric Cancer"]

GSxPTC = GSxPTC.reset_index()
del GSxPTC['index']

NAGxPTC = NAGxPTC.reset_index()
del NAGxPTC['index']

GSxNAG = GSxNAG.reset_index()
del GSxNAG['index']


dataTN


from numpy import mean
from numpy import std
from numpy import arange
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.ensemble import RandomForestClassifier
from matplotlib import pyplot


def getting_datasets():
    X = dataTN.iloc[:,:-1]
    y = dataTN['Phenotype']
    return X, y
 
def getting_models():
    model = dict()
    model['gini'] = RandomForestClassifier(criterion="gini")
    model['entropy'] = RandomForestClassifier(criterion="entropy")

    return model
 
def evaluating_models(model, X, y):
    cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=1)
    scores = cross_val_score(model, X, y, scoring='f1_macro', cv=cv, n_jobs=-1)
    return scores
 
# Calling datasets     
X, y = getting_datasets()

# Initialising models 
modelsTorun = getting_models()

# Saving results form each model 
resultsOfModel, namesOfModel = list(), list()

# Running loop
for names, model in modelsTorun.items():
    # Scoring for each model 
    scores = evaluating_models(model, X, y)
    resultsOfModel.append(scores)
    namesOfModel.append(names)


# plot model performance for comparison
pyplot.boxplot(resultsOfModel, labels=namesOfModel, showmeans=True)
pyplot.xlabel("Criterion")
pyplot.ylabel("Macro F1")
pyplot.title("Tunning for Criterion in Random Forest Classifier")
pyplot.show()


# Getting a dataset
def getting_dataset():
    X = dataTN.iloc[:,:-1]
    y = dataTN['Phenotype']
    return X, y
 
def getting_models():
    model = dict()
    # Tree list 
    trees = [100, 500, 1000,1500,2000]
    # Runing a loop 
    for n in trees:
        model[str(n)] = RandomForestClassifier(n_estimators=n)
    return model
 
# Function for running teh dataset     
def evaluating_models(model, X, y):
    cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=1)
    scores = cross_val_score(model, X, y, scoring='f1_macro', cv=cv, n_jobs=-1)
    return scores
 

# Calling datasets     
X, y = getting_datasets()

# Initialising models 
modelsTorun = getting_models()

# Saving results form each model 
resultsOfModel, namesOfModel = list(), list()

# Running loop
for names, model in modelsTorun.items():
    # Scoring for each model 
    scores = evaluating_models(model, X, y)
    resultsOfModel.append(scores)
    namesOfModel.append(names)

# Plot for model perfirmance
pyplot.boxplot(resultsOfModel, labels=namesOfModel, showmeans=True)
pyplot.xlabel("Number of estimators")
pyplot.ylabel("Macro F1")
pyplot.title("Tunning for Estimators in Random Forest Classifier")
pyplot.show()


def getting_dataset():
    X = dataTN.iloc[:,:-1]
    y = dataTN['Phenotype']
    return X, y
 
# get a list of models to evaluate
def getting_models():
    model = dict()
    depthsOfModel = [depth for depth in range(1, 51)] + [None]
    
    # Running a loop 
    for depth in depthsOfModel:
        model[str(depth)] = RandomForestClassifier(max_depth=depth,n_estimators=1000)
    return model
 

def evaluating_models(model, X, y):
    cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=1)
    scores = cross_val_score(model, X, y, scoring='f1_macro', cv=cv, n_jobs=-1)
    return scores


# Calling datasets     
X, y = getting_datasets()

# Initialising models 
modelsTorun = getting_models()

# Saving results form each model 
resultsOfModel, namesOfModel = list(), list()

# Running loop
for names, model in modelsTorun.items():
    # Scoring for each model 
    scores = evaluating_models(model, X, y)
    resultsOfModel.append(scores)
    namesOfModel.append(names)
    
    
from matplotlib.pyplot import figure

figure(figsize=(18, 8), dpi=80)
pyplot.boxplot(resultsOfModel, labels=namesOfModel, showmeans=True)
pyplot.xlabel("Max Depth")
pyplot.ylabel("Macro F1")
pyplot.title("Tunning for Max Depth in Random Forest Classifier")
pyplot.show()


def getting_dataset():
    X = dataTN.iloc[:,:-1]
    y = dataTN['Phenotype']
    return X, y
 
def getting_models():
    models = dict()

    ls = ["auto"," “sqrt”, “log2",None]
    models["auto/sqrt"] = RandomForestClassifier(max_features="auto",max_depth=20,n_estimators=1000)
    models["log2"] = RandomForestClassifier(max_features="log2",max_depth=20,n_estimators=1000)
    models["None"] = RandomForestClassifier(max_features=None,max_depth=20,n_estimators=1000)
    
    return models
 
def evaluating_models(model, X, y):
    cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=1)
    scores = cross_val_score(model, X, y, scoring='f1_macro', cv=cv, n_jobs=-1)
    return scores


# Calling datasets     
X, y = getting_datasets()

# Initialising models 
modelsTorun = getting_models()

# Saving results form each model 
resultsOfModel, namesOfModel = list(), list()

# Running loop
for names, model in modelsTorun.items():
    # Scoring for each model 
    scores = evaluating_models(model, X, y)
    resultsOfModel.append(scores)
    namesOfModel.append(names)
    
pyplot.boxplot(resultsOfModel, labels=namesOfModel, showmeans=True)
pyplot.xlabel("Max Features")
pyplot.ylabel("Macro F1")
pyplot.title("Tunning for Max Features in Random Forest Classifier")
pyplot.show()

>auto/sqrt 0.642 (0.054)
>log2 0.448 (0.044)
>None 0.662 (0.047)


def getting_dataset():
    X = dataTN.iloc[:,:-1]
    y = dataTN['Phenotype']
    return X, y
 
 
# get a list of models to evaluate
def getting_models():
    model = dict()
    for i in range(1,30,2):
        model[str(i)] = RandomForestClassifier(min_samples_leaf =i,max_features="auto",max_depth=20,n_estimators=1000)
    return model
 
# evaluate a given model using cross-validation
def evaluating_models(model, X, y):
    cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=1)
    scores = cross_val_score(model, X, y, scoring='f1_macro', cv=cv, n_jobs=-1)
    return scores
 

# Calling datasets     
X, y = getting_datasets()

# Initialising models 
modelsTorun = getting_models()

# Saving results form each model 
resultsOfModel, namesOfModel = list(), list()

# Running loop
for names, model in modelsTorun.items():
    # Scoring for each model 
    scores = evaluating_models(model, X, y)
    resultsOfModel.append(scores)
    namesOfModel.append(names)
    # print('>',names and mean(scores) along with standard deviation of scores


pyplot.boxplot(resultsOfModel, labels=namesOfModel, showmeans=True)
pyplot.xlabel("Minimum samples leaf")
pyplot.ylabel("Macro F1")
pyplot.title("Tunning for Minimum samples leaf in Random Forest Classifier")
pyplot.show()


# Using Skicit-learn to split data into training and testing sets
from sklearn.model_selection import train_test_split
import numpy as np
# GSxNAG
# GSxPTC
# NAGxPTC

ENXn = np.array(GSxNAG[[top_varaibles[x] for x in selected_features]])

ENtrain_features, ENtest_features, ENtrain_labels, ENtest_labels = train_test_split(ENXn, np.array(GSxNAG['Phenotype']), test_size = 0.25, random_state = 42)
from sklearn.ensemble import RandomForestClassifier
ENrf = RandomForestClassifier(n_estimators = 1000,max_depth= 20,random_state = 42)
ENrf.fit(ENtrain_features,ENtrain_labels)
ENprediction=ENrf.predict(ENtest_features)

from sklearn import metrics
print("Accuracy:",metrics.accuracy_score(ENtest_labels, ENprediction))
print("F1 macro:",metrics.f1_score(ENtest_labels, ENprediction,average="macro"))
from sklearn.metrics import confusion_matrix
print(confusion_matrix(ENtest_labels, ENprediction))
print(classification_report(ENtest_labels, ENprediction))

Accuracy: 0.9140625
F1 macro: 0.7561904761904762


from sklearn.metrics import confusion_matrix
print(confusion_matrix(ENtest_labels, ENprediction))
print(classification_report(ENtest_labels, ENprediction))

[[  7  11]
 [  0 110]]


# Using Skicit-learn to split data into training and testing sets
from sklearn.model_selection import train_test_split
import numpy as np

ENXn = np.array(dataTN.iloc[:,:-1])


ENtrain_features, ENtest_features, ENtrain_labels, ENtest_labels = train_test_split(ENXn, np.array(dataTN['Phenotype']), test_size = 0.25, random_state = 42)

from sklearn.ensemble import RandomForestClassifier
ENrf = RandomForestClassifier(criterion='gini',max_depth=20,n_estimators=1000,random_state=42,n_jobs=-1)
                          

ENrf.fit(ENtrain_features,ENtrain_labels)

ENprediction=ENrf.predict(ENtest_features)

from sklearn import metrics
print("Accuracy:",metrics.accuracy_score(ENtest_labels, ENprediction))
print("f1_score macro:",metrics.f1_score(ENtest_labels, ENprediction,average="macro"))

Accuracy: 0.7988826815642458
f1_score macro: 0.7095827648356833


import pandas as pd
feature_imp = pd.Series(ENrf.feature_importances_,index=dataTN.iloc[:,:-1].columns).sort_values(ascending=False)


feature_impDF = pd.DataFrame({'Features':feature_imp.index, 'Importance':feature_imp.values})


feature_impDF


import matplotlib.pyplot as plt
import seaborn as sns


# seaborn histogram

sns.distplot(list(feature_imp.values), hist=True, kde=False, 
             bins=800, color = 'blue',
             hist_kws={'edgecolor':'black'}).set(xlabel="VIM",ylabel='Number of Variables',title='Distribution of Variables ')

/home/vbha0006/miniconda3/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

[Text(0.5, 0, 'VIM'),
 Text(0, 0.5, 'Number of Variables'),
 Text(0.5, 1.0, 'Distribution of Variables ')]


import seaborn as sns

top_varaibles_values = list(feature_impDF['Importance'][feature_impDF["Importance"]>0.0002])
# seaborn histogram

sns.distplot(top_varaibles_values, hist=True, kde=False, 
             bins=200, color = 'blue',
             hist_kws={'edgecolor':'black'}).set(xlabel="VIM",ylabel='Number of Variables',title='Distribution of Variables ')

/home/vbha0006/miniconda3/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

[Text(0.5, 0, 'VIM'),
 Text(0, 0.5, 'Number of Variables'),
 Text(0.5, 1.0, 'Distribution of Variables ')]


ENrfRE = RandomForestClassifier(n_estimators = 1000,max_depth= 20,random_state = 42)
info_df=pd.DataFrame()
for i in range(250,1,-1):
    vim_temp = 0.00001*i
    top_varaiblesTesting = list(feature_impDF['Features'][feature_impDF["Importance"]>=vim_temp])
    
    ENXnRe = np.array(dataTN[top_varaiblesTesting])


# Split the data into training and testing sets
    ENRtrain_features, ENRtest_features, ENRtrain_labels, ENRtest_labels = train_test_split(ENXnRe, np.array(dataTN['Phenotype']), test_size = 0.25, random_state = 42)

    ENrfRE.fit(ENRtrain_features,ENRtrain_labels)

    ENRprediction=ENrfRE.predict(ENRtest_features)
    ENRprediction1=ENrfRE.predict(ENRtrain_features)

    mn= pd.DataFrame(columns = ['vim above or equal','accuracy'])
    mn.loc[0,'vim above or equal']=vim_temp
    mn.loc[0,'Test Accuracy']= metrics.accuracy_score(ENRtest_labels, ENRprediction)
    mn.loc[0,'Train Accuracy']= metrics.accuracy_score(ENRtrain_labels, ENRprediction1)

    mn.loc[0,'Number of Variables selected'] = len(top_varaiblesTesting)
    print(mn)
    info_df = pd.concat([info_df,mn],axis=0)

for i in range(19,10,-1):
    vim_temp = 0.000001*i
    top_varaiblesTesting = list(feature_impDF['Features'][feature_impDF["Importance"]>=vim_temp])
    
    ENXnRe = np.array(dataTN[top_varaiblesTesting])


# Split the data into training and testing sets
    ENRtrain_features, ENRtest_features, ENRtrain_labels, ENRtest_labels = train_test_split(ENXnRe, np.array(dataTN['Phenotype']), test_size = 0.25, random_state = 42)
    ENrfRE.fit(ENRtrain_features,ENRtrain_labels)

    ENRprediction=ENrfRE.predict(ENRtest_features)
    ENRprediction1=ENrfRE.predict(ENRtrain_features)

    mn= pd.DataFrame(columns = ['vim above or equal','accuracy'])
    mn.loc[0,'vim above or equal']=vim_temp
    mn.loc[0,'Test Accuracy']= metrics.accuracy_score(ENRtest_labels, ENRprediction)
    mn.loc[0,'Train Accuracy']= metrics.accuracy_score(ENRtrain_labels, ENRprediction1)

    mn.loc[0,'Number of Variables selected'] = len(top_varaiblesTesting)
    
    print(mn)
    info_df = pd.concat([info_df,mn],axis=0)

  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0025      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00249      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00248      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00247      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00246      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00245      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00244      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00243      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00242      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00241      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0024      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00239      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00238      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00237      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00236      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00235      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00234      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00233      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00232      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00231      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0023      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00229      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00228      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00227      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00226      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00225      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00224      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00223      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00222      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00221      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0022      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00219      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00218      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00217      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00216      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00215      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00214      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00213      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00212      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00211      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0021      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00209      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00208      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00207      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00206      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00205      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00204      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00203      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00202      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00201      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0              0.002      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00199      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00198      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00197      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00196      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00195      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00194      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00193      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00192      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00191      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0019      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00189      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00188      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00187      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           1.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00186      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00185      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00184      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00183      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00182      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00181      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0018      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00179      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00178      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00177      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00176      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00175      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00174      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00173      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00172      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00171      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0017      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00169      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00168      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00167      NaN       0.581006        0.590654   

   Number of Variables selected  
0                           2.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00166      NaN       0.564246        0.596262   

   Number of Variables selected  
0                           3.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00165      NaN       0.564246        0.596262   

   Number of Variables selected  
0                           3.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00164      NaN       0.564246        0.596262   

   Number of Variables selected  
0                           3.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00163      NaN       0.564246        0.596262   

   Number of Variables selected  
0                           3.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00162      NaN       0.564246        0.596262   

   Number of Variables selected  
0                           3.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00161      NaN       0.564246        0.596262   

   Number of Variables selected  
0                           3.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0016      NaN       0.564246        0.596262   

   Number of Variables selected  
0                           3.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00159      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00158      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00157      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00156      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00155      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00154      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00153      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00152      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00151      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0015      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00149      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00148      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00147      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00146      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00145      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00144      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00143      NaN       0.636872        0.669159   

   Number of Variables selected  
0                           4.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00142      NaN       0.575419        0.693458   

   Number of Variables selected  
0                           5.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00141      NaN       0.547486        0.719626   

   Number of Variables selected  
0                           6.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0014      NaN       0.592179        0.747664   

   Number of Variables selected  
0                           7.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00139      NaN       0.592179        0.747664   

   Number of Variables selected  
0                           7.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00138      NaN       0.592179        0.747664   

   Number of Variables selected  
0                           7.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00137      NaN       0.592179        0.747664   

   Number of Variables selected  
0                           7.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00136      NaN       0.592179        0.747664   

   Number of Variables selected  
0                           7.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00135      NaN       0.586592        0.805607   

   Number of Variables selected  
0                           8.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00134      NaN       0.586592        0.805607   

   Number of Variables selected  
0                           8.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00133      NaN       0.586592        0.805607   

   Number of Variables selected  
0                           8.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00132      NaN       0.586592        0.805607   

   Number of Variables selected  
0                           8.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00131      NaN       0.586592        0.805607   

   Number of Variables selected  
0                           8.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0013      NaN       0.586592        0.805607   

   Number of Variables selected  
0                           8.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00129      NaN       0.586592        0.805607   

   Number of Variables selected  
0                           8.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00128      NaN       0.586592        0.805607   

   Number of Variables selected  
0                           8.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00127      NaN       0.586592        0.805607   

   Number of Variables selected  
0                           8.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00126      NaN       0.586592        0.805607   

   Number of Variables selected  
0                           8.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00125      NaN       0.586592        0.805607   

   Number of Variables selected  
0                           8.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00124      NaN       0.597765        0.813084   

   Number of Variables selected  
0                           9.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00123      NaN       0.597765        0.813084   

   Number of Variables selected  
0                           9.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00122      NaN       0.597765        0.813084   

   Number of Variables selected  
0                           9.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00121      NaN       0.614525        0.816822   

   Number of Variables selected  
0                          10.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0012      NaN       0.614525        0.816822   

   Number of Variables selected  
0                          10.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00119      NaN       0.614525        0.816822   

   Number of Variables selected  
0                          10.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00118      NaN       0.648045        0.818692   

   Number of Variables selected  
0                          11.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00117      NaN       0.648045        0.818692   

   Number of Variables selected  
0                          11.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00116      NaN       0.648045        0.818692   

   Number of Variables selected  
0                          11.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00115      NaN       0.648045        0.818692   

   Number of Variables selected  
0                          11.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00114      NaN       0.648045        0.818692   

   Number of Variables selected  
0                          11.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00113      NaN       0.648045        0.818692   

   Number of Variables selected  
0                          11.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00112      NaN       0.648045        0.818692   

   Number of Variables selected  
0                          11.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00111      NaN       0.648045        0.818692   

   Number of Variables selected  
0                          11.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0011      NaN       0.608939        0.829907   

   Number of Variables selected  
0                          12.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00109      NaN       0.642458        0.839252   

   Number of Variables selected  
0                          13.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00108      NaN       0.642458        0.839252   

   Number of Variables selected  
0                          13.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00107      NaN       0.625698        0.842991   

   Number of Variables selected  
0                          14.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00106      NaN       0.625698        0.842991   

   Number of Variables selected  
0                          14.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00105      NaN       0.625698        0.842991   

   Number of Variables selected  
0                          14.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00104      NaN       0.625698        0.842991   

   Number of Variables selected  
0                          14.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00103      NaN       0.659218        0.854206   

   Number of Variables selected  
0                          15.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00102      NaN       0.659218        0.854206   

   Number of Variables selected  
0                          16.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00101      NaN       0.659218        0.854206   

   Number of Variables selected  
0                          16.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0              0.001      NaN       0.642458        0.914019   

   Number of Variables selected  
0                          21.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00099      NaN       0.636872        0.914019   

   Number of Variables selected  
0                          22.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00098      NaN       0.648045        0.927103   

   Number of Variables selected  
0                          23.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00097      NaN       0.653631        0.943925   

   Number of Variables selected  
0                          24.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00096      NaN       0.653631        0.943925   

   Number of Variables selected  
0                          24.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00095      NaN       0.653631        0.943925   

   Number of Variables selected  
0                          24.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00094      NaN       0.659218        0.943925   

   Number of Variables selected  
0                          25.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00093      NaN       0.659218        0.943925   

   Number of Variables selected  
0                          25.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00092      NaN       0.681564        0.943925   

   Number of Variables selected  
0                          26.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00091      NaN       0.681564        0.943925   

   Number of Variables selected  
0                          26.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0009      NaN       0.687151         0.95514   

   Number of Variables selected  
0                          27.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00089      NaN       0.709497        0.973832   

   Number of Variables selected  
0                          29.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00088      NaN       0.698324        0.973832   

   Number of Variables selected  
0                          30.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00087      NaN       0.698324        0.973832   

   Number of Variables selected  
0                          30.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00086      NaN       0.726257        0.979439   

   Number of Variables selected  
0                          35.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00085      NaN       0.715084        0.985047   

   Number of Variables selected  
0                          36.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00084      NaN       0.726257        0.988785   

   Number of Variables selected  
0                          40.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00083      NaN       0.726257        0.988785   

   Number of Variables selected  
0                          40.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00082      NaN       0.703911        0.988785   

   Number of Variables selected  
0                          42.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00081      NaN       0.715084        0.986916   

   Number of Variables selected  
0                          44.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0008      NaN       0.709497        0.986916   

   Number of Variables selected  
0                          49.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00079      NaN        0.72067        0.988785   

   Number of Variables selected  
0                          50.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00078      NaN       0.709497        0.986916   

   Number of Variables selected  
0                          51.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00077      NaN        0.72067        0.988785   

   Number of Variables selected  
0                          52.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00076      NaN       0.715084        0.988785   

   Number of Variables selected  
0                          54.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00075      NaN       0.709497        0.988785   

   Number of Variables selected  
0                          55.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00074      NaN       0.748603        0.988785   

   Number of Variables selected  
0                          56.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00073      NaN       0.731844        0.986916   

   Number of Variables selected  
0                          59.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00072      NaN        0.75419        0.986916   

   Number of Variables selected  
0                          63.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00071      NaN       0.748603        0.986916   

   Number of Variables selected  
0                          65.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0007      NaN       0.743017        0.986916   

   Number of Variables selected  
0                          67.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00069      NaN       0.743017        0.986916   

   Number of Variables selected  
0                          69.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00068      NaN        0.75419        0.986916   

   Number of Variables selected  
0                          71.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00067      NaN       0.759777        0.986916   

   Number of Variables selected  
0                          75.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00066      NaN       0.759777        0.986916   

   Number of Variables selected  
0                          76.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00065      NaN        0.75419        0.986916   

   Number of Variables selected  
0                          77.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00064      NaN        0.75419        0.988785   

   Number of Variables selected  
0                          84.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00063      NaN        0.75419        0.988785   

   Number of Variables selected  
0                          86.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00062      NaN       0.748603        0.988785   

   Number of Variables selected  
0                          87.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00061      NaN       0.765363        0.988785   

   Number of Variables selected  
0                          91.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0006      NaN        0.75419        0.988785   

   Number of Variables selected  
0                          98.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00059      NaN        0.75419        0.988785   

   Number of Variables selected  
0                         102.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00058      NaN        0.75419        0.988785   

   Number of Variables selected  
0                         103.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00057      NaN       0.765363        0.988785   

   Number of Variables selected  
0                         105.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00056      NaN        0.75419        0.988785   

   Number of Variables selected  
0                         107.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00055      NaN       0.759777        0.988785   

   Number of Variables selected  
0                         114.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00054      NaN       0.759777        0.988785   

   Number of Variables selected  
0                         118.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00053      NaN       0.748603        0.988785   

   Number of Variables selected  
0                         125.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00052      NaN       0.759777        0.988785   

   Number of Variables selected  
0                         130.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00051      NaN       0.759777        0.988785   

   Number of Variables selected  
0                         139.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0005      NaN       0.765363        0.988785   

   Number of Variables selected  
0                         152.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00049      NaN       0.782123        0.988785   

   Number of Variables selected  
0                         161.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00048      NaN       0.765363        0.988785   

   Number of Variables selected  
0                         170.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00047      NaN       0.776536        0.988785   

   Number of Variables selected  
0                         178.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00046      NaN        0.77095        0.988785   

   Number of Variables selected  
0                         190.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00045      NaN       0.782123        0.988785   

   Number of Variables selected  
0                         196.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00044      NaN       0.776536        0.988785   

   Number of Variables selected  
0                         209.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00043      NaN       0.776536        0.988785   

   Number of Variables selected  
0                         222.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00042      NaN       0.776536        0.988785   

   Number of Variables selected  
0                         232.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00041      NaN       0.776536        0.988785   

   Number of Variables selected  
0                         247.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0004      NaN       0.782123        0.988785   

   Number of Variables selected  
0                         263.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00039      NaN       0.782123        0.988785   

   Number of Variables selected  
0                         281.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00038      NaN       0.782123        0.988785   

   Number of Variables selected  
0                         300.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00037      NaN       0.782123        0.988785   

   Number of Variables selected  
0                         326.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00036      NaN       0.776536        0.988785   

   Number of Variables selected  
0                         346.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00035      NaN       0.782123        0.988785   

   Number of Variables selected  
0                         363.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00034      NaN       0.787709        0.988785   

   Number of Variables selected  
0                         388.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00033      NaN       0.776536        0.988785   

   Number of Variables selected  
0                         421.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00032      NaN       0.793296        0.988785   

   Number of Variables selected  
0                         461.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00031      NaN       0.782123        0.988785   

   Number of Variables selected  
0                         493.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0003      NaN       0.793296        0.988785   

   Number of Variables selected  
0                         536.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00029      NaN       0.793296        0.988785   

   Number of Variables selected  
0                         577.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00028      NaN       0.782123        0.988785   

   Number of Variables selected  
0                         614.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00027      NaN       0.793296        0.988785   

   Number of Variables selected  
0                         663.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00026      NaN       0.787709        0.988785   

   Number of Variables selected  
0                         724.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00025      NaN       0.787709        0.988785   

   Number of Variables selected  
0                         784.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00024      NaN       0.793296        0.988785   

   Number of Variables selected  
0                         852.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00023      NaN       0.787709        0.988785   

   Number of Variables selected  
0                         939.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00022      NaN       0.787709        0.988785   

   Number of Variables selected  
0                        1037.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00021      NaN       0.787709        0.988785   

   Number of Variables selected  
0                        1143.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0002      NaN       0.793296        0.988785   

   Number of Variables selected  
0                        1254.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00019      NaN       0.787709        0.988785   

   Number of Variables selected  
0                        1371.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00018      NaN       0.793296        0.988785   

   Number of Variables selected  
0                        1493.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00017      NaN       0.793296        0.988785   

   Number of Variables selected  
0                        1613.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00016      NaN       0.793296        0.988785   

   Number of Variables selected  
0                        1760.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00015      NaN       0.787709        0.988785   

   Number of Variables selected  
0                        1916.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00014      NaN       0.787709        0.988785   

   Number of Variables selected  
0                        2075.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00013      NaN       0.787709        0.988785   

   Number of Variables selected  
0                        2222.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00012      NaN       0.782123        0.988785   

   Number of Variables selected  
0                        2434.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00011      NaN       0.782123        0.988785   

   Number of Variables selected  
0                        2669.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0             0.0001      NaN       0.787709        0.988785   

   Number of Variables selected  
0                        2925.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00009      NaN       0.787709        0.988785   

   Number of Variables selected  
0                        3263.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00008      NaN       0.787709        0.988785   

   Number of Variables selected  
0                        3619.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00007      NaN       0.787709        0.988785   

   Number of Variables selected  
0                        4005.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00006      NaN       0.793296        0.988785   

   Number of Variables selected  
0                        4517.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00005      NaN       0.798883        0.988785   

   Number of Variables selected  
0                        5134.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00004      NaN       0.782123        0.988785   

   Number of Variables selected  
0                        5915.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00003      NaN       0.787709        0.988785   

   Number of Variables selected  
0                        7081.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0            0.00002      NaN       0.793296        0.988785   

   Number of Variables selected  
0                        8730.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0           0.000019      NaN       0.793296        0.988785   

   Number of Variables selected  
0                        8949.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0           0.000018      NaN       0.787709        0.988785   

   Number of Variables selected  
0                        9198.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0           0.000017      NaN       0.782123        0.988785   

   Number of Variables selected  
0                        9449.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0           0.000016      NaN       0.793296        0.988785   

   Number of Variables selected  
0                        9705.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0           0.000015      NaN       0.787709        0.988785   

   Number of Variables selected  
0                        9907.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0           0.000014      NaN       0.787709        0.988785   

   Number of Variables selected  
0                       10158.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0           0.000013      NaN       0.793296        0.988785   

   Number of Variables selected  
0                       10497.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0           0.000012      NaN       0.793296        0.988785   

   Number of Variables selected  
0                       10896.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0           0.000011      NaN       0.793296        0.988785   

   Number of Variables selected  
0                       11283.0


info_df = info_df.reset_index()

info_dfT = info_df
info_dfT['VIM Above or Equal']=info_dfT['vim above or equal']


import seaborn as sns
sns.scatterplot(data=info_df, y="Test Accuracy", x="Number of Variables selected",hue='VIM Above or Equal').set(title='Accuracy of the Model with VIM above or equal to a specif value')

[Text(0.5, 1.0, 'Accuracy of the Model with VIM above or equal to a specif value')]


fig, ax = plt.subplots()
sns.lineplot(data=info_dfT, y="Train Accuracy", x="Number of Variables selected", label='Train',
               ).set(title='Accuracy of the Model with VIM above or equal to a specific value')
sns.lineplot(data=info_dfT, y="Test Accuracy", x="Number of Variables selected",label='Test', 
             color='r' ).set(title='Accuracy of the Model with varying number of Variables.',ylabel='Acurracy')

[Text(0.5, 1.0, 'Accuracy of the Model with varying number of Variables.'),
 Text(0, 0.5, 'Acurracy')]


fig, ax = plt.subplots()
sns.lineplot(data=info_dfT, y="Train Accuracy", x="VIM Above or Equal", label='Train',
               ).set(title='Accuracy of the Model with VIM above or equal to a specific value')
sns.lineplot(data=info_dfT, y="Test Accuracy", x="VIM Above or Equal",label='Test', 
             color='r' ).set(title='Accuracy of the Model with VIM above or equal to a specific value.',ylabel='Acurracy')

[Text(0.5, 1.0, 'Accuracy of the Model with VIM above or equal to a specific value.'),
 Text(0, 0.5, 'Acurracy')]


top_varaibles = list(feature_impDF['Features'][feature_impDF["Importance"]>0.0002])
len(top_varaibles)

1254


from sklearn.model_selection import train_test_split
import numpy as np

ENXnRe = np.array(dataTN[top_varaibles])


ENRtrain_features, ENRtest_features, ENRtrain_labels, ENRtest_labels = train_test_split(ENXnRe, np.array(dataTN['Phenotype']), test_size = 0.25, random_state = 42)

from sklearn.ensemble import RandomForestClassifier
ENrfRE = RandomForestClassifier(max_features="auto",max_depth=20,n_estimators=1000,random_state = 42)
                            

ENrfRE.fit(ENRtrain_features,ENRtrain_labels)

ENRprediction=ENrfRE.predict(ENRtest_features)

from sklearn import metrics
print("Accuracy:",metrics.accuracy_score(ENRtest_labels, ENRprediction))

Accuracy: 0.7932960893854749


from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import spearmanr
from scipy.cluster import hierarchy
from sklearn.ensemble import RandomForestClassifier
from sklearn.inspection import permutation_importance
from sklearn.model_selection import train_test_split


fig, (ax1) = plt.subplots(figsize=(60,10))
corr_Spearman_linkage = hierarchy.ward(spearmanr(ENXnRe).correlation)
dendro = hierarchy.dendrogram(
    corr_Spearman_linkage, labels=top_varaibles, ax=ax1, leaf_rotation=90, leaf_font_size=15, truncate_mode = 'level', p=7
)

dendro_idx = np.arange(0, len(dendro['ivl']))
fig.tight_layout()
fig.tight_layout()
plt.show()


fig, (ax1) = plt.subplots(figsize=(100,50))

corr_Spearman_linkage = hierarchy.ward(spearmanr(ENXnRe).correlation)

dendrogram_plot = hierarchy.dendrogram(
    corr_Spearman_linkage, labels=top_varaibles, ax=ax1, leaf_rotation=90, leaf_font_size=8)

dendro_idx = np.arange(0, len(dendrogram_plot['ivl']))
# plotting
fig.tight_layout()
fig.tight_layout()
plt.show()


fig, (ax1) = plt.subplots(figsize=(30,100))

corr_Spearman_linkage = hierarchy.ward(spearmanr(ENXnRe).correlation)

dendrogram_plot = hierarchy.dendrogram(
    corr_Spearman_linkage, labels=top_varaibles, ax=ax1, leaf_font_size=8, orientation="left")

dendro_idx = np.arange(0, len(dendrogram_plot['ivl']))
# plotting
fig.tight_layout()
fig.tight_layout()
plt.show()


clt_id2fea_ids = defaultdict(list)
# selrting distance as "4"
clt_ids = hierarchy.fcluster(corr_Spearman_linkage, 4, criterion='distance')

for index, clt_id in enumerate(clt_ids):
    clt_id2fea_ids[clt_id].append(index)
    
selected_features = [i[0] for i in clt_id2fea_ids.values()]


len(selected_features)

142


from sklearn.model_selection import train_test_split
import numpy as np

ENXnRet = np.array(dataTN[[top_varaibles[x] for x in selected_features]])


# Split the data into training and testing sets
ENRtrain_featuresT, ENRtest_featuresT, ENRtrain_labelsT, ENRtest_labelsT = train_test_split(ENXnRet, 
                                                                                            np.array(dataTN['Phenotype']), 
                                                                                            test_size = 0.25, 
                                                                                            random_state = 42)

from sklearn.ensemble import RandomForestClassifier
# Instantiate model with 1000 decision trees
ENrfRET = RandomForestClassifier(max_features="auto",max_depth=20,n_estimators=1000,random_state = 42)
                             #, min_samples_split=5,max_terminal_nodes=10, min_samples_leaf=200,random_state = 42)
# Train the model on training data

ENrfRET.fit(ENRtrain_featuresT,ENRtrain_labelsT)

ENRpredictionT=ENrfRET.predict(ENRtest_featuresT)

from sklearn import metrics
print("Accuracy:",metrics.accuracy_score(ENRtest_labelsT, ENRpredictionT))
print("Accuracy:",metrics.f1_score(ENRtest_labelsT, ENRpredictionT,average="macro"))

Accuracy: 0.8044692737430168
Accuracy: 0.7326959752566724


from yellowbrick.classifier import ROCAUC

def plot_ROC_curve(model, xtrain, ytrain, xtest, ytest):

    vis = ROCAUC(model)
                                        
    vis.fit(xtrain, ytrain)
    vis.score(xtest, ytest)
    vis.show()


plot_ROC_curve(ENrfRET,ENRtrain_featuresT,ENRtrain_labelsT,ENRtest_featuresT,ENRtest_labelsT )


from sklearn.model_selection import train_test_split
import numpy as np


ENXn = np.array(GSxPTC[[top_varaibles[x] for x in selected_features]])


ENtrain_features, ENtest_features, ENtrain_labels, ENtest_labels = train_test_split(ENXn, np.array(GSxPTC['Phenotype']), test_size = 0.25, random_state = 42)

from sklearn.ensemble import RandomForestClassifier
ENrf = RandomForestClassifier(n_estimators = 1000,max_depth= 20,random_state = 42, class_weight='balanced')
                           
ENrf.fit(ENtrain_features,ENtrain_labels)

ENprediction=ENrf.predict(ENtest_features)

#Import scikit-learn metrics module for accuracy calculation
from sklearn import metrics
# Model Accuracy, how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(ENtest_labels, ENprediction))
print("F1 macro:",metrics.f1_score(ENtest_labels, ENprediction,average="macro"))

Accuracy: 0.7866666666666666
F1 macro: 0.7272727272727273


from sklearn.model_selection import train_test_split
import numpy as np
# GSxNAG
# GSxPTC
# NAGxPTC

ENXn = np.array(NAGxPTC[[top_varaibles[x] for x in selected_features]])


ENtrain_features, ENtest_features, ENtrain_labels, ENtest_labels = train_test_split(ENXn, np.array(NAGxPTC['Phenotype']), test_size = 0.20, random_state = 42)

from sklearn.ensemble import RandomForestClassifier
ENrf = RandomForestClassifier(n_estimators = 1000,max_depth= 20,random_state = 42)
                           

ENrf.fit(ENtrain_features,ENtrain_labels)

ENprediction=ENrf.predict(ENtest_features)

from sklearn import metrics
print("Accuracy:",metrics.accuracy_score(ENtest_labels, ENprediction))
print("F1 macro:",metrics.f1_score(ENtest_labels, ENprediction,average="macro"))

Accuracy: 0.8870967741935484
F1 macro: 0.8630050505050505


class_weightm = dict({'Gastric Cancer':4, "Non Atrophic Gastritis":1
                      , "Progressive towards Cancer":2})

from sklearn.model_selection import train_test_split
import numpy as np

ENXnRet = np.array(dataTN[[top_varaibles[x] for x in selected_features]])


# Split the data into training and testing sets
ENRtrain_featuresT, ENRtest_featuresT, ENRtrain_labelsT, ENRtest_labelsT = train_test_split(ENXnRet, 
                                                                                            np.array(dataTN['Phenotype']),
                                                                                            test_size = 0.25, 
                                                                                            random_state = 42)

from sklearn.ensemble import RandomForestClassifier
ENrfRET = RandomForestClassifier(n_estimators = 1000,max_depth= 20,random_state = 42,class_weight=class_weightm)
                            

ENrfRET.fit(ENRtrain_featuresT,ENRtrain_labelsT)

ENRpredictionT=ENrfRET.predict(ENRtest_featuresT)

from sklearn import metrics
print("Accuracy:",metrics.accuracy_score(ENRtest_labelsT, ENRpredictionT))


print(classification_report(ENRtest_labelsT, ENRpredictionT))

Accuracy: 0.770949720670391
                            precision    recall  f1-score   support

            Gastric Cancer       1.00      0.46      0.63        24
    Non Atrophic Gastritis       0.78      0.90      0.84       114
Progressive towards Cancer       0.67      0.59      0.62        41

                  accuracy                           0.77       179
                 macro avg       0.82      0.65      0.70       179
              weighted avg       0.78      0.77      0.76       179


feature_imp1 = pd.Series(ENrfRET.feature_importances_,index=dataTN[[top_varaibles[x] for x in selected_features]].iloc[:,:].columns).sort_values(ascending=False)

feature_impDF1 = pd.DataFrame({'Features':feature_imp1.index, 'Importance':feature_imp1.values})


feature_impDF1[:10]


feat_mine = BorutaPy(ENrfRET, n_estimators ='auto', verbose=2, random_state=1,max_iter = 50,perc=80,two_step =False)


feat_mine.fit(ENRtrain_featuresT,ENRtrain_labelsT)

Iteration: 	1 / 50
Confirmed: 	0
Tentative: 	142
Rejected: 	0
Iteration: 	2 / 50
Confirmed: 	0
Tentative: 	142
Rejected: 	0
Iteration: 	3 / 50
Confirmed: 	0
Tentative: 	142
Rejected: 	0
Iteration: 	4 / 50
Confirmed: 	0
Tentative: 	142
Rejected: 	0
Iteration: 	5 / 50
Confirmed: 	0
Tentative: 	142
Rejected: 	0
Iteration: 	6 / 50
Confirmed: 	0
Tentative: 	142
Rejected: 	0
Iteration: 	7 / 50
Confirmed: 	0
Tentative: 	142
Rejected: 	0
Iteration: 	8 / 50
Confirmed: 	0
Tentative: 	142
Rejected: 	0
Iteration: 	9 / 50
Confirmed: 	0
Tentative: 	142
Rejected: 	0
Iteration: 	10 / 50
Confirmed: 	0
Tentative: 	142
Rejected: 	0
Iteration: 	11 / 50
Confirmed: 	0
Tentative: 	142
Rejected: 	0
Iteration: 	12 / 50
Confirmed: 	46
Tentative: 	88
Rejected: 	8
Iteration: 	13 / 50
Confirmed: 	46
Tentative: 	88
Rejected: 	8
Iteration: 	14 / 50
Confirmed: 	46
Tentative: 	88
Rejected: 	8
Iteration: 	15 / 50
Confirmed: 	46
Tentative: 	88
Rejected: 	8
Iteration: 	16 / 50
Confirmed: 	54
Tentative: 	76
Rejected: 	12
Iteration: 	17 / 50
Confirmed: 	54
Tentative: 	76
Rejected: 	12
Iteration: 	18 / 50
Confirmed: 	54
Tentative: 	76
Rejected: 	12
Iteration: 	19 / 50
Confirmed: 	54
Tentative: 	76
Rejected: 	12
Iteration: 	20 / 50
Confirmed: 	58
Tentative: 	71
Rejected: 	13
Iteration: 	21 / 50
Confirmed: 	58
Tentative: 	71
Rejected: 	13
Iteration: 	22 / 50
Confirmed: 	58
Tentative: 	71
Rejected: 	13
Iteration: 	23 / 50
Confirmed: 	63
Tentative: 	62
Rejected: 	17
Iteration: 	24 / 50
Confirmed: 	63
Tentative: 	62
Rejected: 	17
Iteration: 	25 / 50
Confirmed: 	63
Tentative: 	62
Rejected: 	17
Iteration: 	26 / 50
Confirmed: 	63
Tentative: 	59
Rejected: 	20
Iteration: 	27 / 50
Confirmed: 	63
Tentative: 	59
Rejected: 	20
Iteration: 	28 / 50
Confirmed: 	63
Tentative: 	59
Rejected: 	20
Iteration: 	29 / 50
Confirmed: 	66
Tentative: 	54
Rejected: 	22
Iteration: 	30 / 50
Confirmed: 	66
Tentative: 	54
Rejected: 	22
Iteration: 	31 / 50
Confirmed: 	66
Tentative: 	54
Rejected: 	22
Iteration: 	32 / 50
Confirmed: 	66
Tentative: 	52
Rejected: 	24
Iteration: 	33 / 50
Confirmed: 	66
Tentative: 	52
Rejected: 	24
Iteration: 	34 / 50
Confirmed: 	66
Tentative: 	52
Rejected: 	24
Iteration: 	35 / 50
Confirmed: 	67
Tentative: 	48
Rejected: 	27
Iteration: 	36 / 50
Confirmed: 	67
Tentative: 	48
Rejected: 	27
Iteration: 	37 / 50
Confirmed: 	67
Tentative: 	48
Rejected: 	27
Iteration: 	38 / 50
Confirmed: 	67
Tentative: 	46
Rejected: 	29
Iteration: 	39 / 50
Confirmed: 	67
Tentative: 	46
Rejected: 	29
Iteration: 	40 / 50
Confirmed: 	69
Tentative: 	40
Rejected: 	33
Iteration: 	41 / 50
Confirmed: 	69
Tentative: 	40
Rejected: 	33
Iteration: 	42 / 50
Confirmed: 	69
Tentative: 	40
Rejected: 	33
Iteration: 	43 / 50
Confirmed: 	71
Tentative: 	37
Rejected: 	34
Iteration: 	44 / 50
Confirmed: 	71
Tentative: 	37
Rejected: 	34
Iteration: 	45 / 50
Confirmed: 	71
Tentative: 	37
Rejected: 	34
Iteration: 	46 / 50
Confirmed: 	71
Tentative: 	36
Rejected: 	35
Iteration: 	47 / 50
Confirmed: 	71
Tentative: 	36
Rejected: 	35
Iteration: 	48 / 50
Confirmed: 	71
Tentative: 	36
Rejected: 	35
Iteration: 	49 / 50
Confirmed: 	72
Tentative: 	35
Rejected: 	35


BorutaPy finished running.

Iteration: 	50 / 50
Confirmed: 	72
Tentative: 	16
Rejected: 	35


from boruta import BorutaPy
feature_ranks101 = list(zip(selected_features, 
                         feat_mine.ranking_, 
                         feat_mine.support_))

for feat in feature_ranks101:
    print(feat[0], feat[1], feat[2])

Feature: 0                         Rank: 1,  Keep: True
Feature: 1                         Rank: 1,  Keep: True
Feature: 2                         Rank: 1,  Keep: True
Feature: 3                         Rank: 1,  Keep: True
Feature: 4                         Rank: 1,  Keep: True
Feature: 6                         Rank: 14,  Keep: False
Feature: 7                         Rank: 1,  Keep: True
Feature: 8                         Rank: 1,  Keep: True
Feature: 9                         Rank: 1,  Keep: True
Feature: 10                        Rank: 1,  Keep: True
Feature: 11                        Rank: 1,  Keep: True
Feature: 13                        Rank: 1,  Keep: True
Feature: 14                        Rank: 1,  Keep: True
Feature: 16                        Rank: 1,  Keep: True
Feature: 17                        Rank: 1,  Keep: True
Feature: 18                        Rank: 1,  Keep: True
Feature: 19                        Rank: 1,  Keep: True
Feature: 20                        Rank: 1,  Keep: True
Feature: 22                        Rank: 1,  Keep: True
Feature: 23                        Rank: 1,  Keep: True
Feature: 25                        Rank: 1,  Keep: True
Feature: 26                        Rank: 2,  Keep: False
Feature: 27                        Rank: 1,  Keep: True
Feature: 28                        Rank: 1,  Keep: True
Feature: 29                        Rank: 462,  Keep: False
Feature: 31                        Rank: 1,  Keep: True
Feature: 33                        Rank: 1,  Keep: True
Feature: 34                        Rank: 1,  Keep: True
Feature: 35                        Rank: 109,  Keep: False
Feature: 36                        Rank: 208,  Keep: False
Feature: 37                        Rank: 1,  Keep: True
Feature: 40                        Rank: 2,  Keep: False
Feature: 43                        Rank: 290,  Keep: False
Feature: 47                        Rank: 25,  Keep: False
Feature: 48                        Rank: 1,  Keep: True
Feature: 50                        Rank: 72,  Keep: False
Feature: 51                        Rank: 158,  Keep: False
Feature: 53                        Rank: 194,  Keep: False
Feature: 54                        Rank: 18,  Keep: False
Feature: 55                        Rank: 491,  Keep: False
Feature: 56                        Rank: 573,  Keep: False
Feature: 59                        Rank: 174,  Keep: False
Feature: 60                        Rank: 2,  Keep: False
Feature: 62                        Rank: 37,  Keep: False
Feature: 63                        Rank: 1,  Keep: True
Feature: 65                        Rank: 2,  Keep: False
Feature: 66                        Rank: 1,  Keep: True
Feature: 68                        Rank: 588,  Keep: False
Feature: 70                        Rank: 269,  Keep: False
Feature: 71                        Rank: 1,  Keep: True
Feature: 73                        Rank: 186,  Keep: False
Feature: 75                        Rank: 1,  Keep: True
Feature: 76                        Rank: 1,  Keep: True
Feature: 79                        Rank: 2,  Keep: False
Feature: 81                        Rank: 1,  Keep: True
Feature: 82                        Rank: 665,  Keep: False
Feature: 83                        Rank: 1,  Keep: True
Feature: 89                        Rank: 1,  Keep: True
Feature: 91                        Rank: 143,  Keep: False
Feature: 92                        Rank: 29,  Keep: False
Feature: 93                        Rank: 59,  Keep: False
Feature: 94                        Rank: 1,  Keep: True
Feature: 97                        Rank: 228,  Keep: False
Feature: 98                        Rank: 521,  Keep: False
Feature: 100                       Rank: 1,  Keep: True
Feature: 102                       Rank: 3,  Keep: False
Feature: 103                       Rank: 188,  Keep: False
Feature: 104                       Rank: 2,  Keep: False
Feature: 105                       Rank: 144,  Keep: False
Feature: 110                       Rank: 238,  Keep: False
Feature: 112                       Rank: 2,  Keep: False
Feature: 113                       Rank: 328,  Keep: False
Feature: 114                       Rank: 116,  Keep: False
Feature: 115                       Rank: 29,  Keep: False
Feature: 116                       Rank: 352,  Keep: False
Feature: 117                       Rank: 100,  Keep: False
Feature: 118                       Rank: 1,  Keep: True
Feature: 122                       Rank: 1,  Keep: True
Feature: 131                       Rank: 67,  Keep: False
Feature: 139                       Rank: 212,  Keep: False
Feature: 144                       Rank: 306,  Keep: False
Feature: 147                       Rank: 1087,  Keep: False
Feature: 148                       Rank: 169,  Keep: False
Feature: 149                       Rank: 92,  Keep: False
Feature: 151                       Rank: 1,  Keep: True
Feature: 155                       Rank: 82,  Keep: False
Feature: 160                       Rank: 2,  Keep: False
Feature: 161                       Rank: 204,  Keep: False
Feature: 162                       Rank: 1196,  Keep: False
Feature: 163                       Rank: 1034,  Keep: False
Feature: 166                       Rank: 1,  Keep: True
Feature: 180                       Rank: 67,  Keep: False
Feature: 183                       Rank: 1888,  Keep: False
Feature: 189                       Rank: 137,  Keep: False
Feature: 193                       Rank: 152,  Keep: False
Feature: 195                       Rank: 1,  Keep: True
Feature: 203                       Rank: 64,  Keep: False
Feature: 208                       Rank: 1,  Keep: True
Feature: 216                       Rank: 723,  Keep: False
Feature: 218                       Rank: 236,  Keep: False
Feature: 224                       Rank: 369,  Keep: False
Feature: 228                       Rank: 479,  Keep: False
Feature: 229                       Rank: 573,  Keep: False
Feature: 233                       Rank: 129,  Keep: False
Feature: 234                       Rank: 467,  Keep: False
Feature: 249                       Rank: 1,  Keep: True
Feature: 255                       Rank: 1,  Keep: True
Feature: 256                       Rank: 29,  Keep: False
Feature: 259                       Rank: 1,  Keep: True
Feature: 280                       Rank: 497,  Keep: False
Feature: 282                       Rank: 230,  Keep: False
Feature: 283                       Rank: 1473,  Keep: False
Feature: 286                       Rank: 621,  Keep: False
Feature: 288                       Rank: 477,  Keep: False
Feature: 301                       Rank: 442,  Keep: False
Feature: 302                       Rank: 119,  Keep: False
Feature: 317                       Rank: 1,  Keep: True
Feature: 324                       Rank: 118,  Keep: False
Feature: 343                       Rank: 522,  Keep: False
Feature: 344                       Rank: 1,  Keep: True
Feature: 348                       Rank: 1,  Keep: True
Feature: 385                       Rank: 18,  Keep: False
Feature: 394                       Rank: 153,  Keep: False
Feature: 402                       Rank: 470,  Keep: False
Feature: 403                       Rank: 25,  Keep: False
Feature: 422                       Rank: 200,  Keep: False
Feature: 434                       Rank: 402,  Keep: False
Feature: 448                       Rank: 410,  Keep: False
Feature: 467                       Rank: 492,  Keep: False
Feature: 475                       Rank: 252,  Keep: False
Feature: 478                       Rank: 334,  Keep: False
Feature: 491                       Rank: 566,  Keep: False
Feature: 547                       Rank: 2,  Keep: False
Feature: 568                       Rank: 612,  Keep: False
Feature: 598                       Rank: 279,  Keep: False
Feature: 612                       Rank: 1,  Keep: True
Feature: 634                       Rank: 673,  Keep: False
Feature: 661                       Rank: 1,  Keep: True
Feature: 701                       Rank: 32,  Keep: False
Feature: 702                       Rank: 300,  Keep: False
Feature: 773                       Rank: 670,  Keep: False
Feature: 910                       Rank: 810,  Keep: False


final_variable_list = [top_varaibles[x] for x in selected_features]


len(selected_features)


final_variable_list[:5]

['221331.T/C', '962858.G/A', '1115013.G/A,T', '712033.A/C,G,T', '908373.T/C']


read_variant_dict_gtf = pd.read_csv(r'../data/new_gtf.txt' ,error_bad_lines=False, sep="\t",header=None)

read_variant_dict_gtf = read_variant_dict_gtf[read_variant_dict_gtf[2]=="gene"]

for index, row in read_variant_dict_gtf.iterrows():
    
    read_variant_dict_gtf.loc[index, "gene_name" ]=read_variant_dict_gtf.loc[index,8].split()[5][1:-2]

read_variant_dict_gtf = read_variant_dict_gtf[read_variant_dict_gtf["gene_name"]!="protein_coding"]


read_variant_dict_gtf


temp_gtf = pd.read_csv(r'../data/new_gtf.txt' ,error_bad_lines=False, sep="\t",header=None)
#temp_gtf = temp_gtf[temp_gtf[2]=="CDS"]


temp_gtf[8][1]

'gene_id "HPP12_0001"; transcript_id "unknown_transcript_1"; gbkey "CDS"; gene "nusB"; locus_tag "HPP12_0001"; note "transcriptional antitermination factor"; product "N utilization substance protein B"; protein_id "ACJ07161.1"; transl_table "11"; '


for i in temp_gtf[8][1].strip(" ").split(";")[:-1]:
    print(i.strip().split(" ",1)[1])
    
for index, row in temp_gtf.iterrows():
    for i in temp_gtf[8][index].strip(" ").split(";")[:-1]:

        temp_gtf.loc[index, i.strip().split(" ",1)[0]]=i.strip().split(" ",1)[1]

"HPP12_0001"
"unknown_transcript_1"
"CDS"
"nusB"
"HPP12_0001"
"transcriptional antitermination factor"
"N utilization substance protein B"
"ACJ07161.1"
"11"


temp_gtf[8][0]

'gene_id "HPP12_0001"; gbkey "Gene"; gene "nusB"; gene_biotype "protein_coding"; locus_tag "HPP12_0001"; '


read_variant_dict_gff = pd.read_csv(r'../data/new_gff.txt' ,error_bad_lines=False, sep="\t",header=None)
read_variant_dict_gff = read_variant_dict_gff[read_variant_dict_gff[2]=="gene"]

for index, row in read_variant_dict_gff.iterrows():
    
    read_variant_dict_gff.loc[index, "gene_name" ]=read_variant_dict_gff.loc[index,8].split(";")[1][5:]
    
    
read_variant_dict_gff = read_variant_dict_gff[read_variant_dict_gff["gene_name"]

read_variant_dict_gff = pd.read_csv(r'../data/new_gff.txt' ,error_bad_lines=False, sep="\t",header=None)

read_variant_dict_gff[8][1].split(";")[1][5:]


final_variable_df = pd.DataFrame(final_variable_list)


for index, row in final_variable_df.iterrows():
    
    final_variable_df.loc[index, 1 ]=int(final_variable_df.loc[index,0].split(".")[0])
    final_variable_df.loc[index, 2 ]=final_variable_df.loc[index,0].split(".")[1]


final_variable_df


final_variable_df[:10]


feature_impDF1[:10]


temp_gtf_final = pd.DataFrame()
#for i in list(final_variable_df[1]):
for index, row in final_variable_df.iterrows():

    temp_gtfi = temp_gtf[(temp_gtf[3]<= final_variable_df.loc[index, 1 ]) & (temp_gtf[4]>=final_variable_df.loc[index, 1 ])]
    temp_gtfi = temp_gtfi[(temp_gtfi[2]=="CDS")]
    temp_gtfi['Phenotype']=final_variable_df.loc[index, 0 ]
    temp_gtfi['CDS Start'] = temp_gtfi[3]
    temp_gtfi['CDS End'] = temp_gtfi[4]
    temp_gtfi['Gene ID'] = temp_gtfi['gene_id']
    temp_gtfi['Product'] = temp_gtfi['product']
    print(temp_gtfi)
    #print(len(temp_gtfi))
    temp_gtf_final = pd.concat([temp_gtf_final, temp_gtfi], axis=0)

              0        1    2       3       4  5  6  7  \
872  CP001217.1  Genbank  CDS  220874  221671  .  +  0   

                                                     8       gene_id  ...  \
872  gene_id "HPP12_0216"; transcript_id "unknown_t...  "HPP12_0216"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
872         NaN      NaN       NaN         NaN     NaN  221331.T/C    220874   

    CDS End       Gene ID                          Product  
872  221671  "HPP12_0216"  "cdp-diacylglycerol synthetase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3651  CP001217.1  Genbank  CDS  962720  964273  .  +  0   

                                                      8       gene_id  ...  \
3651  gene_id "HPP12_0910"; transcript_id "unknown_t...  "HPP12_0910"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3651         NaN      NaN       NaN         NaN     NaN  962858.G/A    962720   

     CDS End       Gene ID                             Product  
3651  964273  "HPP12_0910"  "outer membrane protein HopC/AlpA"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4207  CP001217.1  Genbank  CDS  1114613  1115281  .  -  0   

                                                      8       gene_id  ...  \
4207  gene_id "HPP12_1044"; transcript_id "unknown_t...  "HPP12_1044"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4207         NaN      NaN       NaN         NaN     NaN  1115013.G/A,T   

     CDS Start  CDS End       Gene ID                                Product  
4207   1114613  1115281  "HPP12_1044"  "thiol:disulfide interchange protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
2678  CP001217.1  Genbank  CDS  709540  712278  .  +  0   

                                                      8       gene_id  ...  \
2678  gene_id "HPP12_0668"; transcript_id "unknown_t...  "HPP12_0668"  ...   

     exon_number probably exception interrupted missing       Phenotype  \
2678         NaN      NaN       NaN         NaN     NaN  712033.A/C,G,T   

     CDS Start CDS End       Gene ID                           Product  
2678    709540  712278  "HPP12_0668"  "protective surface antigen D15"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3439  CP001217.1  Genbank  CDS  908318  909700  .  -  0   

                                                      8       gene_id  ...  \
3439  gene_id "HPP12_0858"; transcript_id "unknown_t...  "HPP12_0858"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3439         NaN      NaN       NaN         NaN     NaN  908373.T/C    908318   

     CDS End       Gene ID                 Product  
3439  909700  "HPP12_0858"  "ADP-heptose synthase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4473  CP001217.1  Genbank  CDS  1181129  1182427  .  -  0   

                                                      8       gene_id  ...  \
4473  gene_id "HPP12_1109"; transcript_id "unknown_t...  "HPP12_1109"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4473         NaN      NaN       NaN         NaN     NaN  1181754.C/A,T   

     CDS Start  CDS End       Gene ID                 Product  
4473   1181129  1182427  "HPP12_1109"  "hypothetical protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
1186  CP001217.1  Genbank  CDS  309196  311679  .  -  0   

                                                      8       gene_id  ...  \
1186  gene_id "HPP12_0294"; transcript_id "unknown_t...  "HPP12_0294"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
1186         NaN      NaN       NaN         NaN     NaN  310663.A/G,T   

     CDS Start CDS End       Gene ID                                Product  
1186    309196  311679  "HPP12_0294"  "flagellar-hook associated protein 3"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3309  CP001217.1  Genbank  CDS  878490  879128  .  +  0   

                                                      8       gene_id  ...  \
3309  gene_id "HPP12_0826"; transcript_id "unknown_t...  "HPP12_0826"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3309         NaN      NaN       NaN         NaN     NaN  878577.G/A    878490   

     CDS End       Gene ID                           Product  
3309  879128  "HPP12_0826"  "osmoprotection ABC transporter"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
5131  CP001217.1  Genbank  CDS  1353416  1353949  .  -  0   

                                                      8       gene_id  ...  \
5131  gene_id "HPP12_1269"; transcript_id "unknown_t...  "HPP12_1269"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
5131         NaN      NaN       NaN         NaN     NaN  1353700.T/C   

     CDS Start  CDS End       Gene ID                     Product  
5131   1353416  1353949  "HPP12_1269"  "50S ribosomal protein L6"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3651  CP001217.1  Genbank  CDS  962720  964273  .  +  0   

                                                      8       gene_id  ...  \
3651  gene_id "HPP12_0910"; transcript_id "unknown_t...  "HPP12_0910"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3651         NaN      NaN       NaN         NaN     NaN  962897.G/A    962720   

     CDS End       Gene ID                             Product  
3651  964273  "HPP12_0910"  "outer membrane protein HopC/AlpA"  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
471  CP001217.1  Genbank  CDS  119554  119847  .  +  0   

                                                     8       gene_id  ...  \
471  gene_id "HPP12_0115"; transcript_id "unknown_t...  "HPP12_0115"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
471         NaN      NaN       NaN         NaN     NaN  119674.A/G    119554   

    CDS End       Gene ID                 Product  
471  119847  "HPP12_0115"  "hypothetical protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
1271  CP001217.1  Genbank  CDS  331407  333029  .  -  0   

                                                      8       gene_id  ...  \
1271  gene_id "HPP12_0316"; transcript_id "unknown_t...  "HPP12_0316"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
1271         NaN      NaN       NaN         NaN     NaN  332096.C/T    331407   

     CDS End       Gene ID                    Product  
1271  333029  "HPP12_0316"  "arginyl-tRNA synthetase"  

[1 rows x 29 columns]
              0        1    2      3      4  5  6  7  \
147  CP001217.1  Genbank  CDS  37520  38260  .  +  0   

                                                     8       gene_id  ...  \
147  gene_id "HPP12_0035"; transcript_id "unknown_t...  "HPP12_0035"  ...   

    exon_number probably exception interrupted missing  Phenotype CDS Start  \
147         NaN      NaN       NaN         NaN     NaN  37710.C/T     37520   

    CDS End       Gene ID                     Product  
147   38260  "HPP12_0035"  "ComB8 competence protein"  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
699  CP001217.1  Genbank  CDS  177456  178352  .  +  0   

                                                     8       gene_id  ...  \
699  gene_id "HPP12_0172"; transcript_id "unknown_t...  "HPP12_0172"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
699         NaN      NaN       NaN         NaN     NaN  178004.T/C    177456   

    CDS End       Gene ID                                  Product  
699  178352  "HPP12_0172"  "peptidyl-prolyl cis-trans isomerase C"  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
860  CP001217.1  Genbank  CDS  216095  217243  .  -  0   

                                                     8       gene_id  ...  \
860  gene_id "HPP12_0213"; transcript_id "unknown_t...  "HPP12_0213"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
860         NaN      NaN       NaN         NaN     NaN  216651.A/G    216095   

    CDS End       Gene ID                                   Product  
860  217243  "HPP12_0213"  "succinyl-diaminopimelate desuccinylase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3121  CP001217.1  Genbank  CDS  828445  829518  .  +  0   

                                                      8       gene_id  ...  \
3121  gene_id "HPP12_0779"; transcript_id "unknown_t...  "HPP12_0779"  ...   

     exon_number probably exception interrupted missing       Phenotype  \
3121         NaN      NaN       NaN         NaN     NaN  828717.T/A,C,G   

     CDS Start CDS End       Gene ID                           Product  
3121    828445  829518  "HPP12_0779"  "flagellar biosynthetic protein"  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
860  CP001217.1  Genbank  CDS  216095  217243  .  -  0   

                                                     8       gene_id  ...  \
860  gene_id "HPP12_0213"; transcript_id "unknown_t...  "HPP12_0213"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
860         NaN      NaN       NaN         NaN     NaN  216718.C/T    216095   

    CDS End       Gene ID                                   Product  
860  217243  "HPP12_0213"  "succinyl-diaminopimelate desuccinylase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3687  CP001217.1  Genbank  CDS  976759  983874  .  +  0   

                                                      8       gene_id  ...  \
3687  gene_id "HPP12_0919"; transcript_id "unknown_t...  "HPP12_0919"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3687         NaN      NaN       NaN         NaN     NaN  981671.G/A    976759   

     CDS End       Gene ID                                    Product  
3687  983874  "HPP12_0919"  "vacuolating cytotoxin VacA-like protein"  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
563  CP001217.1  Genbank  CDS  146167  147813  .  +  0   

                                                     8       gene_id  ...  \
563  gene_id "HPP12_0138"; transcript_id "unknown_t...  "HPP12_0138"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
563         NaN      NaN       NaN         NaN     NaN  147200.T/C    146167   

    CDS End       Gene ID               Product  
563  147813  "HPP12_0138"  "L-lactate permease"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
3811  CP001217.1  Genbank  CDS  1008993  1009646  .  +  0   

                                                      8       gene_id  ...  \
3811  gene_id "HPP12_0948"; transcript_id "unknown_t...  "HPP12_0948"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
3811         NaN      NaN       NaN         NaN     NaN  1009179.C/A,T   

     CDS Start  CDS End       Gene ID                      Product  
3811   1008993  1009646  "HPP12_0948"  "integral membrane protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
5647  CP001217.1  Genbank  CDS  1480565  1481635  .  -  0   

                                                      8       gene_id  ...  \
5647  gene_id "HPP12_1400"; transcript_id "unknown_t...  "HPP12_1400"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
5647         NaN      NaN       NaN         NaN     NaN  1481143.G/A   

     CDS Start  CDS End       Gene ID                                 Product  
5647   1480565  1481635  "HPP12_1400"  "radical SAM domain-containing enzyme"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
2478  CP001217.1  Genbank  CDS  660696  661811  .  +  0   

                                                      8       gene_id  ...  \
2478  gene_id "HPP12_0618"; transcript_id "unknown_t...  "HPP12_0618"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
2478         NaN      NaN       NaN         NaN     NaN  661042.G/A    660696   

     CDS End       Gene ID                                    Product  
2478  661811  "HPP12_0618"  "vacuolating cytotoxin VacA-like protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
1070  CP001217.1  Genbank  CDS  272839  273972  .  +  0   

                                                      8       gene_id  ...  \
1070  gene_id "HPP12_0265"; transcript_id "unknown_t...  "HPP12_0265"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
1070         NaN      NaN       NaN         NaN     NaN  273458.A/G,T   

     CDS Start CDS End       Gene ID           Product  
1070    272839  273972  "HPP12_0265"  "dihydroorotase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4207  CP001217.1  Genbank  CDS  1114613  1115281  .  -  0   

                                                      8       gene_id  ...  \
4207  gene_id "HPP12_1044"; transcript_id "unknown_t...  "HPP12_1044"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4207         NaN      NaN       NaN         NaN     NaN  1114836.G/A,T   

     CDS Start  CDS End       Gene ID                                Product  
4207   1114613  1115281  "HPP12_1044"  "thiol:disulfide interchange protein"  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
467  CP001217.1  Genbank  CDS  118895  119530  .  +  0   

                                                     8       gene_id  ...  \
467  gene_id "HPP12_0114"; transcript_id "unknown_t...  "HPP12_0114"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
467         NaN      NaN       NaN         NaN     NaN  119199.C/T    118895   

    CDS End       Gene ID                            Product  
467  119530  "HPP12_0114"  "L-fuculose-1-phosphate aldolase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3587  CP001217.1  Genbank  CDS  947918  949027  .  -  0   

                                                      8       gene_id  ...  \
3587  gene_id "HPP12_0894"; transcript_id "unknown_t...  "HPP12_0894"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
3587         NaN      NaN       NaN         NaN     NaN  948406.G/A,T   

     CDS Start CDS End       Gene ID  \
3587    947918  949027  "HPP12_0894"   

                                              Product  
3587  "hydrogenase expression/formation protein HypD"  

[1 rows x 29 columns]
              0        1    2      3      4  5  6  7  \
171  CP001217.1  Genbank  CDS  44879  45874  .  -  0   

                                                     8       gene_id  ...  \
171  gene_id "HPP12_0041"; transcript_id "unknown_t...  "HPP12_0041"  ...   

    exon_number probably exception interrupted missing  Phenotype CDS Start  \
171         NaN      NaN       NaN         NaN     NaN  45533.C/T     44879   

    CDS End       Gene ID                                     Product  
171   45874  "HPP12_0041"  "hydrogenase expression/formation protein"  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
739  CP001217.1  Genbank  CDS  185786  187288  .  +  0   

                                                     8       gene_id  ...  \
739  gene_id "HPP12_0182"; transcript_id "unknown_t...  "HPP12_0182"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
739         NaN      NaN       NaN         NaN     NaN  186902.A/C    185786   

    CDS End       Gene ID                  Product  
739  187288  "HPP12_0182"  "lysyl-tRNA synthetase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
3815  CP001217.1  Genbank  CDS  1009655  1010218  .  -  0   

                                                      8       gene_id  ...  \
3815  gene_id "HPP12_0949"; transcript_id "unknown_t...  "HPP12_0949"  ...   

     exon_number probably exception interrupted missing        Phenotype  \
3815         NaN      NaN       NaN         NaN     NaN  1010062.G/A,C,T   

     CDS Start  CDS End       Gene ID                 Product  
3815   1009655  1010218  "HPP12_0949"  "hypothetical protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3189  CP001217.1  Genbank  CDS  850011  851507  .  +  0   

                                                      8       gene_id  ...  \
3189  gene_id "HPP12_0796"; transcript_id "unknown_t...  "HPP12_0796"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3189         NaN      NaN       NaN         NaN     NaN  850608.G/C    850011   

     CDS End       Gene ID                        Product  
3189  851507  "HPP12_0796"  "outer membrane protein HofF"  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
799  CP001217.1  Genbank  CDS  199662  200669  .  +  0   

                                                     8       gene_id  ...  \
799  gene_id "HPP12_0197"; transcript_id "unknown_t...  "HPP12_0197"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
799         NaN      NaN       NaN         NaN     NaN  199666.A/G    199662   

    CDS End       Gene ID                                            Product  
799  200669  "HPP12_0197"  "UDP-3-O-(3-hydroxymyristoyl) glucosamine N-ac...  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3715  CP001217.1  Genbank  CDS  988669  989577  .  +  0   

                                                      8       gene_id  ...  \
3715  gene_id "HPP12_0926"; transcript_id "unknown_t...  "HPP12_0926"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3715         NaN      NaN       NaN         NaN     NaN  989254.A/G    988669   

     CDS End       Gene ID                                 Product  
3715  989577  "HPP12_0926"  "trans-isoprenyl diphosphate synthase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3077  CP001217.1  Genbank  CDS  819259  820569  .  +  0   

                                                      8       gene_id  ...  \
3077  gene_id "HPP12_0768"; transcript_id "unknown_t...  "HPP12_0768"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
3077         NaN      NaN       NaN         NaN     NaN  819317.G/A,C   

     CDS Start CDS End       Gene ID                     Product  
3077    819259  820569  "HPP12_0768"  "permease/na-h antiporter"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4273  CP001217.1  Genbank  CDS  1129555  1130586  .  -  0   

                                                      8       gene_id  ...  \
4273  gene_id "HPP12_1060"; transcript_id "unknown_t...  "HPP12_1060"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4273         NaN      NaN       NaN         NaN     NaN  1130013.T/A,C   

     CDS Start  CDS End       Gene ID                    Product  
4273   1129555  1130586  "HPP12_1060"  "UDP-glucose 4-epimerase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
1142  CP001217.1  Genbank  CDS  289503  291071  .  +  0   

                                                      8       gene_id  ...  \
1142  gene_id "HPP12_0283"; transcript_id "unknown_t...  "HPP12_0283"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
1142         NaN      NaN       NaN         NaN     NaN  290613.A/G    289503   

     CDS End       Gene ID                                 Product  
1142  291071  "HPP12_0283"  "mechanosensitive ion channel protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3631  CP001217.1  Genbank  CDS  955244  956260  .  +  0   

                                                      8       gene_id  ...  \
3631  gene_id "HPP12_0905"; transcript_id "unknown_t...  "HPP12_0905"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3631         NaN      NaN       NaN         NaN     NaN  955719.A/C    955244   

     CDS End       Gene ID                            Product  
3631  956260  "HPP12_0905"  "flagellar hook assembly protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4207  CP001217.1  Genbank  CDS  1114613  1115281  .  -  0   

                                                      8       gene_id  ...  \
4207  gene_id "HPP12_1044"; transcript_id "unknown_t...  "HPP12_1044"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4207         NaN      NaN       NaN         NaN     NaN  1114964.G/A,T   

     CDS Start  CDS End       Gene ID                                Product  
4207   1114613  1115281  "HPP12_1044"  "thiol:disulfide interchange protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4313  CP001217.1  Genbank  CDS  1140422  1141465  .  +  0   

                                                      8       gene_id  ...  \
4313  gene_id "HPP12_1069"; transcript_id "unknown_t...  "HPP12_1069"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4313         NaN      NaN       NaN         NaN     NaN  1140792.G/A   

     CDS Start  CDS End       Gene ID                   Product  
4313   1140422  1141465  "HPP12_1069"  "mannitol dehydrogenase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
3891  CP001217.1  Genbank  CDS  1029242  1031344  .  -  0   

                                                      8       gene_id  ...  \
3891  gene_id "HPP12_0968"; transcript_id "unknown_t...  "HPP12_0968"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
3891         NaN      NaN       NaN         NaN     NaN  1030162.G/A   

     CDS Start  CDS End       Gene ID                                Product  
3891   1029242  1031344  "HPP12_0968"  "glycyl-tRNA synthetase beta subunit"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3317  CP001217.1  Genbank  CDS  879776  881557  .  +  0   

                                                      8       gene_id  ...  \
3317  gene_id "HPP12_0828"; transcript_id "unknown_t...  "HPP12_0828"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
3317         NaN      NaN       NaN         NaN     NaN  880392.G/A,T   

     CDS Start CDS End       Gene ID                       Product  
3317    879776  881557  "HPP12_0828"  "excinuclease ABC subunit C"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
1003  CP001217.1  Genbank  CDS  254135  255220  .  +  0   

                                                      8       gene_id  ...  \
1003  gene_id "HPP12_0248"; transcript_id "unknown_t...  "HPP12_0248"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
1003         NaN      NaN       NaN         NaN     NaN  254291.G/A    254135   

     CDS End       Gene ID                           Product  
1003  255220  "HPP12_0248"  "spfH domain-containing protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
1230  CP001217.1  Genbank  CDS  320919  322208  .  +  0   

                                                      8       gene_id  ...  \
1230  gene_id "HPP12_0305"; transcript_id "unknown_t...  "HPP12_0305"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
1230         NaN      NaN       NaN         NaN     NaN  322164.G/A,C   

     CDS Start CDS End       Gene ID  \
1230    320919  322208  "HPP12_0305"   

                                         Product  
1230  "glutamate-1-semialdehyde 2,1-aminomutase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
5215  CP001217.1  Genbank  CDS  1365240  1365614  .  +  0   

                                                      8       gene_id  ...  \
5215  gene_id "HPP12_1290"; transcript_id "unknown_t...  "HPP12_1290"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
5215         NaN      NaN       NaN         NaN     NaN  1365456.C/A,T   

     CDS Start  CDS End       Gene ID                 Product  
5215   1365240  1365614  "HPP12_1290"  "hypothetical protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
2510  CP001217.1  Genbank  CDS  667150  668880  .  +  0   

                                                      8       gene_id  ...  \
2510  gene_id "HPP12_0626"; transcript_id "unknown_t...  "HPP12_0626"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
2510         NaN      NaN       NaN         NaN     NaN  668224.G/A,T   

     CDS Start CDS End       Gene ID                     Product  
2510    667150  668880  "HPP12_0626"  "aspartyl-tRNA synthetase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3535  CP001217.1  Genbank  CDS  933103  934560  .  +  0   

                                                      8       gene_id  ...  \
3535  gene_id "HPP12_0882"; transcript_id "unknown_t...  "HPP12_0882"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3535         NaN      NaN       NaN         NaN     NaN  934087.C/T    933103   

     CDS End       Gene ID                          Product  
3535  934560  "HPP12_0882"  "virulence factor MviN protein"  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
411  CP001217.1  Genbank  CDS  101620  103071  .  -  0   

                                                     8       gene_id  ...  \
411  gene_id "HPP12_0100"; transcript_id "unknown_t...  "HPP12_0100"  ...   

    exon_number probably exception interrupted missing     Phenotype  \
411         NaN      NaN       NaN         NaN     NaN  102230.G/A,T   

    CDS Start CDS End       Gene ID               Product  
411    101620  103071  "HPP12_0100"  "threonine synthase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4103  CP001217.1  Genbank  CDS  1085069  1087360  .  +  0   

                                                      8       gene_id  ...  \
4103  gene_id "HPP12_1018"; transcript_id "unknown_t...  "HPP12_1018"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4103         NaN      NaN       NaN         NaN     NaN  1086551.G/A   

     CDS Start  CDS End       Gene ID  \
4103   1085069  1087360  "HPP12_1018"   

                                          Product  
4103  "phenylalanyl-tRNA synthetase beta subunit"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4289  CP001217.1  Genbank  CDS  1133809  1134678  .  -  0   

                                                      8       gene_id  ...  \
4289  gene_id "HPP12_1063"; transcript_id "unknown_t...  "HPP12_1063"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4289         NaN      NaN       NaN         NaN     NaN  1134198.T/A,C   

     CDS Start  CDS End       Gene ID                    Product  
4289   1133809  1134678  "HPP12_1063"  "cysteine-rich protein C"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
5007  CP001217.1  Genbank  CDS  1324216  1325751  .  +  0   

                                                      8       gene_id  ...  \
5007  gene_id "HPP12_1238"; transcript_id "unknown_t...  "HPP12_1238"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
5007         NaN      NaN       NaN         NaN     NaN  1325191.G/A   

     CDS Start  CDS End       Gene ID  \
5007   1324216  1325751  "HPP12_1238"   

                                       Product  
5007  "NADH-ubiquinone oxidoreductase chain M"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4551  CP001217.1  Genbank  CDS  1203139  1203717  .  -  0   

                                                      8       gene_id  ...  \
4551  gene_id "HPP12_1128"; transcript_id "unknown_t...  "HPP12_1128"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4551         NaN      NaN       NaN         NaN     NaN  1203164.T/C,G   

     CDS Start  CDS End       Gene ID                      Product  
4551   1203139  1203717  "HPP12_1128"  "integral membrane protein"  

[1 rows x 29 columns]
Empty DataFrame
Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, gene_id, gbkey, gene, gene_biotype, locus_tag, transcript_id, note, product, protein_id, transl_table, exon_number, probably, exception, interrupted, missing, Phenotype, CDS Start, CDS End, Gene ID, Product]
Index: []

[0 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
3879  CP001217.1  Genbank  CDS  1023822  1026878  .  -  0   

                                                      8       gene_id  ...  \
3879  gene_id "HPP12_0965"; transcript_id "unknown_t...  "HPP12_0965"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
3879         NaN      NaN       NaN         NaN     NaN  1026812.C/T   

     CDS Start  CDS End       Gene ID  \
3879   1023822  1026878  "HPP12_0965"   

                                       Product  
3879  "cobalt-zinc-cadmium resistance protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
5131  CP001217.1  Genbank  CDS  1353416  1353949  .  -  0   

                                                      8       gene_id  ...  \
5131  gene_id "HPP12_1269"; transcript_id "unknown_t...  "HPP12_1269"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
5131         NaN      NaN       NaN         NaN     NaN  1353699.C/G,T   

     CDS Start  CDS End       Gene ID                     Product  
5131   1353416  1353949  "HPP12_1269"  "50S ribosomal protein L6"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4401  CP001217.1  Genbank  CDS  1164613  1165605  .  -  0   

                                                      8       gene_id  ...  \
4401  gene_id "HPP12_1091"; transcript_id "unknown_t...  "HPP12_1091"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4401         NaN      NaN       NaN         NaN     NaN  1165040.T/C   

     CDS Start  CDS End       Gene ID                 Product  
4401   1164613  1165605  "HPP12_1091"  "hypothetical protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3651  CP001217.1  Genbank  CDS  962720  964273  .  +  0   

                                                      8       gene_id  ...  \
3651  gene_id "HPP12_0910"; transcript_id "unknown_t...  "HPP12_0910"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3651         NaN      NaN       NaN         NaN     NaN  963647.C/T    962720   

     CDS End       Gene ID                             Product  
3651  964273  "HPP12_0910"  "outer membrane protein HopC/AlpA"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
1325  CP001217.1  Genbank  CDS  342744  343547  .  +  0   

                                                      8       gene_id  ...  \
1325  gene_id "HPP12_0328"; transcript_id "unknown_t...  "HPP12_0328"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
1325         NaN      NaN       NaN         NaN     NaN  343320.C/T    342744   

     CDS End       Gene ID                                 Product  
1325  343547  "HPP12_0328"  "MinD cell division inhibitor protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4309  CP001217.1  Genbank  CDS  1139242  1140249  .  +  0   

                                                      8       gene_id  ...  \
4309  gene_id "HPP12_1068"; transcript_id "unknown_t...  "HPP12_1068"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4309         NaN      NaN       NaN         NaN     NaN  1139533.A/G   

     CDS Start  CDS End       Gene ID        Product  
4309   1139242  1140249  "HPP12_1068"  "glucokinase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4297  CP001217.1  Genbank  CDS  1135395  1137218  .  -  0   

                                                      8       gene_id  ...  \
4297  gene_id "HPP12_1065"; transcript_id "unknown_t...  "HPP12_1065"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4297         NaN      NaN       NaN         NaN     NaN  1135538.T/C   

     CDS Start  CDS End       Gene ID                           Product  
4297   1135395  1137218  "HPP12_1065"  "6-phosphogluconate dehydratase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4991  CP001217.1  Genbank  CDS  1320869  1321528  .  +  0   

                                                      8       gene_id  ...  \
4991  gene_id "HPP12_1234"; transcript_id "unknown_t...  "HPP12_1234"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4991         NaN      NaN       NaN         NaN     NaN  1320905.T/A,G   

     CDS Start  CDS End       Gene ID  \
4991   1320869  1321528  "HPP12_1234"   

                                       Product  
4991  "NADH-ubiquinone oxidoreductase chain I"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
5655  CP001217.1  Genbank  CDS  1482608  1484680  .  -  0   

                                                      8       gene_id  ...  \
5655  gene_id "HPP12_1402"; transcript_id "unknown_t...  "HPP12_1402"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
5655         NaN      NaN       NaN         NaN     NaN  1484166.G/A   

     CDS Start  CDS End       Gene ID                    Product  
5655   1482608  1484680  "HPP12_1402"  "ATP/GTP binding protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4551  CP001217.1  Genbank  CDS  1203139  1203717  .  -  0   

                                                      8       gene_id  ...  \
4551  gene_id "HPP12_1128"; transcript_id "unknown_t...  "HPP12_1128"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4551         NaN      NaN       NaN         NaN     NaN  1203201.A/G   

     CDS Start  CDS End       Gene ID                      Product  
4551   1203139  1203717  "HPP12_1128"  "integral membrane protein"  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
963  CP001217.1  Genbank  CDS  244614  246344  .  -  0   

                                                     8       gene_id  ...  \
963  gene_id "HPP12_0238"; transcript_id "unknown_t...  "HPP12_0238"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
963         NaN      NaN       NaN         NaN     NaN  245831.T/C    244614   

    CDS End       Gene ID                   Product  
963  246344  "HPP12_0238"  "prolyl-tRNA synthetase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4269  CP001217.1  Genbank  CDS  1128830  1129555  .  +  0   

                                                      8       gene_id  ...  \
4269  gene_id "HPP12_1059"; transcript_id "unknown_t...  "HPP12_1059"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4269         NaN      NaN       NaN         NaN     NaN  1129019.C/T   

     CDS Start  CDS End       Gene ID                       Product  
4269   1128830  1129555  "HPP12_1059"  "pseudouridylate synthase I"  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
743  CP001217.1  Genbank  CDS  187291  188538  .  +  0   

                                                     8       gene_id  ...  \
743  gene_id "HPP12_0183"; transcript_id "unknown_t...  "HPP12_0183"  ...   

    exon_number probably exception interrupted missing     Phenotype  \
743         NaN      NaN       NaN         NaN     NaN  188024.C/A,T   

    CDS Start CDS End       Gene ID                            Product  
743    187291  188538  "HPP12_0183"  "serine hydroxymethyltransferase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
2638  CP001217.1  Genbank  CDS  699208  700887  .  +  0   

                                                      8       gene_id  ...  \
2638  gene_id "HPP12_0658"; transcript_id "unknown_t...  "HPP12_0658"  ...   

     exon_number probably exception interrupted missing       Phenotype  \
2638         NaN      NaN       NaN         NaN     NaN  699442.T/A,C,G   

     CDS Start CDS End       Gene ID                                  Product  
2638    699208  700887  "HPP12_0658"  "soluble lytic murein transglycosylase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4269  CP001217.1  Genbank  CDS  1128830  1129555  .  +  0   

                                                      8       gene_id  ...  \
4269  gene_id "HPP12_1059"; transcript_id "unknown_t...  "HPP12_1059"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4269         NaN      NaN       NaN         NaN     NaN  1129043.G/A,T   

     CDS Start  CDS End       Gene ID                       Product  
4269   1128830  1129555  "HPP12_1059"  "pseudouridylate synthase I"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4035  CP001217.1  Genbank  CDS  1066380  1067546  .  -  0   

                                                      8       gene_id  ...  \
4035  gene_id "HPP12_1003"; transcript_id "unknown_t...  "HPP12_1003"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4035         NaN      NaN       NaN         NaN     NaN  1066523.G/A,T   

     CDS Start  CDS End       Gene ID  \
4035   1066380  1067546  "HPP12_1003"   

                                                Product  
4035  "type 1 capsular polysaccharide biosynthesis p...  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4281  CP001217.1  Genbank  CDS  1131284  1132816  .  +  0   

                                                      8       gene_id  ...  \
4281  gene_id "HPP12_1061"; transcript_id "unknown_t...  "HPP12_1061"  ...   

     exon_number probably exception interrupted missing        Phenotype  \
4281         NaN      NaN       NaN         NaN     NaN  1131910.G/A,C,T   

     CDS Start  CDS End       Gene ID                   Product  
4281   1131284  1132816  "HPP12_1061"  "outer membrane protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4269  CP001217.1  Genbank  CDS  1128830  1129555  .  +  0   

                                                      8       gene_id  ...  \
4269  gene_id "HPP12_1059"; transcript_id "unknown_t...  "HPP12_1059"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4269         NaN      NaN       NaN         NaN     NaN  1129042.T/C   

     CDS Start  CDS End       Gene ID                       Product  
4269   1128830  1129555  "HPP12_1059"  "pseudouridylate synthase I"  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
579  CP001217.1  Genbank  CDS  150764  152209  .  -  0   

                                                     8       gene_id  ...  \
579  gene_id "HPP12_0142"; transcript_id "unknown_t...  "HPP12_0142"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
579         NaN      NaN       NaN         NaN     NaN  151693.T/C    150764   

    CDS End       Gene ID                                            Product  
579  152209  "HPP12_0142"  "sodium:sulfate symporter transmembrane region...  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3137  CP001217.1  Genbank  CDS  832821  834026  .  -  0   

                                                      8       gene_id  ...  \
3137  gene_id "HPP12_0783"; transcript_id "unknown_t...  "HPP12_0783"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3137         NaN      NaN       NaN         NaN     NaN  833990.T/C    832821   

     CDS End       Gene ID                    Product  
3137  834026  "HPP12_0783"  "tyrosyl-tRNA synthetase"  

[1 rows x 29 columns]
Empty DataFrame
Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, gene_id, gbkey, gene, gene_biotype, locus_tag, transcript_id, note, product, protein_id, transl_table, exon_number, probably, exception, interrupted, missing, Phenotype, CDS Start, CDS End, Gene ID, Product]
Index: []

[0 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3687  CP001217.1  Genbank  CDS  976759  983874  .  +  0   

                                                      8       gene_id  ...  \
3687  gene_id "HPP12_0919"; transcript_id "unknown_t...  "HPP12_0919"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
3687         NaN      NaN       NaN         NaN     NaN  979096.G/A,T   

     CDS Start CDS End       Gene ID  \
3687    976759  983874  "HPP12_0919"   

                                        Product  
3687  "vacuolating cytotoxin VacA-like protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4103  CP001217.1  Genbank  CDS  1085069  1087360  .  +  0   

                                                      8       gene_id  ...  \
4103  gene_id "HPP12_1018"; transcript_id "unknown_t...  "HPP12_1018"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4103         NaN      NaN       NaN         NaN     NaN  1085754.A/G   

     CDS Start  CDS End       Gene ID  \
4103   1085069  1087360  "HPP12_1018"   

                                          Product  
4103  "phenylalanyl-tRNA synthetase beta subunit"  

[1 rows x 29 columns]
              0        1    2      3      4  5  6  7  \
123  CP001217.1  Genbank  CDS  32458  34680  .  +  0   

                                                     8       gene_id  ...  \
123  gene_id "HPP12_0029"; transcript_id "unknown_t...  "HPP12_0029"  ...   

    exon_number probably exception interrupted missing  Phenotype CDS Start  \
123         NaN      NaN       NaN         NaN     NaN  34261.A/G     32458   

    CDS End       Gene ID                       Product  
123   34680  "HPP12_0029"  "ATP-dependent clp protease"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4911  CP001217.1  Genbank  CDS  1302135  1304066  .  -  0   

                                                      8       gene_id  ...  \
4911  gene_id "HPP12_1214"; transcript_id "unknown_t...  "HPP12_1214"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4911         NaN      NaN       NaN         NaN     NaN  1303684.T/C   

     CDS Start  CDS End       Gene ID                    Product  
4911   1302135  1304066  "HPP12_1214"  "3'-5' exoribonuclease R"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3503  CP001217.1  Genbank  CDS  921962  922834  .  -  0   

                                                      8       gene_id  ...  \
3503  gene_id "HPP12_0874"; transcript_id "unknown_t...  "HPP12_0874"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3503         NaN      NaN       NaN         NaN     NaN  922177.G/A    921962   

     CDS End       Gene ID                 Product  
3503  922834  "HPP12_0874"  "hypothetical protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
2514  CP001217.1  Genbank  CDS  668900  669472  .  +  0   

                                                      8       gene_id  ...  \
2514  gene_id "HPP12_0627"; transcript_id "unknown_t...  "HPP12_0627"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
2514         NaN      NaN       NaN         NaN     NaN  669143.G/A,T   

     CDS Start CDS End       Gene ID             Product  
2514    668900  669472  "HPP12_0627"  "adenylate kinase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4203  CP001217.1  Genbank  CDS  1111794  1114601  .  -  0   

                                                      8       gene_id  ...  \
4203  gene_id "HPP12_1043"; transcript_id "unknown_t...  "HPP12_1043"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4203         NaN      NaN       NaN         NaN     NaN  1113967.A/G   

     CDS Start  CDS End       Gene ID                                 Product  
4203   1111794  1114601  "HPP12_1043"  "cytochrome C-type biogenesis protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4127  CP001217.1  Genbank  CDS  1093432  1095279  .  +  0   

                                                      8       gene_id  ...  \
4127  gene_id "HPP12_1024"; transcript_id "unknown_t...  "HPP12_1024"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4127         NaN      NaN       NaN         NaN     NaN  1093801.A/G   

     CDS Start  CDS End       Gene ID  \
4127   1093432  1095279  "HPP12_1024"   

                                            Product  
4127  "3-octaprenyl-4-hydroxybenzoate carboxylyase"  

[1 rows x 29 columns]
Empty DataFrame
Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, gene_id, gbkey, gene, gene_biotype, locus_tag, transcript_id, note, product, protein_id, transl_table, exon_number, probably, exception, interrupted, missing, Phenotype, CDS Start, CDS End, Gene ID, Product]
Index: []

[0 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3085  CP001217.1  Genbank  CDS  821892  823478  .  -  0   

                                                      8       gene_id  ...  \
3085  gene_id "HPP12_0770"; transcript_id "unknown_t...  "HPP12_0770"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
3085         NaN      NaN       NaN         NaN     NaN  822520.G/A,T   

     CDS Start CDS End       Gene ID                 Product  
3085    821892  823478  "HPP12_0770"  "hypothetical protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
5007  CP001217.1  Genbank  CDS  1324216  1325751  .  +  0   

                                                      8       gene_id  ...  \
5007  gene_id "HPP12_1238"; transcript_id "unknown_t...  "HPP12_1238"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
5007         NaN      NaN       NaN         NaN     NaN  1324835.T/C   

     CDS Start  CDS End       Gene ID  \
5007   1324216  1325751  "HPP12_1238"   

                                       Product  
5007  "NADH-ubiquinone oxidoreductase chain M"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3439  CP001217.1  Genbank  CDS  908318  909700  .  -  0   

                                                      8       gene_id  ...  \
3439  gene_id "HPP12_0858"; transcript_id "unknown_t...  "HPP12_0858"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
3439         NaN      NaN       NaN         NaN     NaN  908686.G/C,T   

     CDS Start CDS End       Gene ID                 Product  
3439    908318  909700  "HPP12_0858"  "ADP-heptose synthase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
2245  CP001217.1  Genbank  CDS  592387  593247  .  +  0   

                                                      8       gene_id  ...  \
2245  gene_id "HPP12_0559"; transcript_id "unknown_t...  "HPP12_0559"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
2245         NaN      NaN       NaN         NaN     NaN  592853.A/G    592387   

     CDS End       Gene ID      Product  
2245  593247  "HPP12_0559"  "methylase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4203  CP001217.1  Genbank  CDS  1111794  1114601  .  -  0   

                                                      8       gene_id  ...  \
4203  gene_id "HPP12_1043"; transcript_id "unknown_t...  "HPP12_1043"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4203         NaN      NaN       NaN         NaN     NaN  1114376.C/T   

     CDS Start  CDS End       Gene ID                                 Product  
4203   1111794  1114601  "HPP12_1043"  "cytochrome C-type biogenesis protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4207  CP001217.1  Genbank  CDS  1114613  1115281  .  -  0   

                                                      8       gene_id  ...  \
4207  gene_id "HPP12_1044"; transcript_id "unknown_t...  "HPP12_1044"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4207         NaN      NaN       NaN         NaN     NaN  1114957.T/C   

     CDS Start  CDS End       Gene ID                                Product  
4207   1114613  1115281  "HPP12_1044"  "thiol:disulfide interchange protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4807  CP001217.1  Genbank  CDS  1275869  1278706  .  -  0   

                                                      8       gene_id  ...  \
4807  gene_id "HPP12_1188"; transcript_id "unknown_t...  "HPP12_1188"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4807         NaN      NaN       NaN         NaN     NaN  1278445.T/C   

     CDS Start  CDS End       Gene ID                    Product  
4807   1275869  1278706  "HPP12_1188"  "D-lactate dehydrogenase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3333  CP001217.1  Genbank  CDS  883595  884527  .  +  0   

                                                      8       gene_id  ...  \
3333  gene_id "HPP12_0832"; transcript_id "unknown_t...  "HPP12_0832"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3333         NaN      NaN       NaN         NaN     NaN  884241.G/A    883595   

     CDS End       Gene ID                  Product  
3333  884527  "HPP12_0832"  "thioredoxin reductase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4907  CP001217.1  Genbank  CDS  1301123  1302142  .  -  0   

                                                      8       gene_id  ...  \
4907  gene_id "HPP12_1213"; transcript_id "unknown_t...  "HPP12_1213"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4907         NaN      NaN       NaN         NaN     NaN  1302018.C/T   

     CDS Start  CDS End       Gene ID  \
4907   1301123  1302142  "HPP12_1213"   

                                            Product  
4907  "DNA polymerase III holoenzyme delta subunit"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4313  CP001217.1  Genbank  CDS  1140422  1141465  .  +  0   

                                                      8       gene_id  ...  \
4313  gene_id "HPP12_1069"; transcript_id "unknown_t...  "HPP12_1069"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4313         NaN      NaN       NaN         NaN     NaN  1141155.A/C,G   

     CDS Start  CDS End       Gene ID                   Product  
4313   1140422  1141465  "HPP12_1069"  "mannitol dehydrogenase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3053  CP001217.1  Genbank  CDS  814657  816711  .  +  0   

                                                      8       gene_id  ...  \
3053  gene_id "HPP12_0762"; transcript_id "unknown_t...  "HPP12_0762"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
3053         NaN      NaN       NaN         NaN     NaN  815966.A/C,T   

     CDS Start CDS End       Gene ID                                Product  
3053    814657  816711  "HPP12_0762"  "flagellar hook-associated protein 2"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4091  CP001217.1  Genbank  CDS  1082350  1083669  .  -  0   

                                                      8       gene_id  ...  \
4091  gene_id "HPP12_1015"; transcript_id "unknown_t...  "HPP12_1015"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4091         NaN      NaN       NaN         NaN     NaN  1083619.C/T   

     CDS Start  CDS End       Gene ID              Product  
4091   1082350  1083669  "HPP12_1015"  "nifs-like protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3383  CP001217.1  Genbank  CDS  895508  896122  .  +  0   

                                                      8       gene_id  ...  \
3383  gene_id "HPP12_0844"; transcript_id "unknown_t...  "HPP12_0844"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3383         NaN      NaN       NaN         NaN     NaN  895560.T/C    895508   

     CDS End       Gene ID                 Product  
3383  896122  "HPP12_0844"  "hypothetical protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
2694  CP001217.1  Genbank  CDS  716075  717316  .  +  0   

                                                      8       gene_id  ...  \
2694  gene_id "HPP12_0672"; transcript_id "unknown_t...  "HPP12_0672"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
2694         NaN      NaN       NaN         NaN     NaN  716615.G/A    716075   

     CDS End       Gene ID                 Product  
2694  717316  "HPP12_0672"  "hypothetical protein"  

[1 rows x 29 columns]
Empty DataFrame
Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, gene_id, gbkey, gene, gene_biotype, locus_tag, transcript_id, note, product, protein_id, transl_table, exon_number, probably, exception, interrupted, missing, Phenotype, CDS Start, CDS End, Gene ID, Product]
Index: []

[0 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4075  CP001217.1  Genbank  CDS  1077048  1078571  .  -  0   

                                                      8       gene_id  ...  \
4075  gene_id "HPP12_1011"; transcript_id "unknown_t...  "HPP12_1011"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4075         NaN      NaN       NaN         NaN     NaN  1078108.C/G,T   

     CDS Start  CDS End       Gene ID         Product  
4075   1077048  1078571  "HPP12_1011"  "gmp synthase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3675  CP001217.1  Genbank  CDS  971491  974745  .  -  0   

                                                      8       gene_id  ...  \
3675  gene_id "HPP12_0916"; transcript_id "unknown_t...  "HPP12_0916"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
3675         NaN      NaN       NaN         NaN     NaN  973382.G/A,T   

     CDS Start CDS End       Gene ID  \
3675    971491  974745  "HPP12_0916"   

                                           Product  
3675  "carbamoyl-phosphate synthase large subunit"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4071  CP001217.1  Genbank  CDS  1076211  1076960  .  -  0   

                                                      8       gene_id  ...  \
4071  gene_id "HPP12_1010"; transcript_id "unknown_t...  "HPP12_1010"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4071         NaN      NaN       NaN         NaN     NaN  1076835.A/C,T   

     CDS Start  CDS End       Gene ID                     Product  
4071   1076211  1076960  "HPP12_1010"  "flagellar sheath adhesin"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3455  CP001217.1  Genbank  CDS  911944  912612  .  -  0   

                                                      8       gene_id  ...  \
3455  gene_id "HPP12_0862"; transcript_id "unknown_t...  "HPP12_0862"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3455         NaN      NaN       NaN         NaN     NaN  912462.T/C    911944   

     CDS End       Gene ID                Product  
3455  912612  "HPP12_0862"  "pantothenate kinase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4531  CP001217.1  Genbank  CDS  1196934  1200632  .  +  0   

                                                      8       gene_id  ...  \
4531  gene_id "HPP12_1123"; transcript_id "unknown_t...  "HPP12_1123"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4531         NaN      NaN       NaN         NaN     NaN  1197609.A/G   

     CDS Start  CDS End       Gene ID                        Product  
4531   1196934  1200632  "HPP12_1123"  "outer membrane protein HopL"  

[1 rows x 29 columns]
Empty DataFrame
Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, gene_id, gbkey, gene, gene_biotype, locus_tag, transcript_id, note, product, protein_id, transl_table, exon_number, probably, exception, interrupted, missing, Phenotype, CDS Start, CDS End, Gene ID, Product]
Index: []

[0 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
2478  CP001217.1  Genbank  CDS  660696  661811  .  +  0   

                                                      8       gene_id  ...  \
2478  gene_id "HPP12_0618"; transcript_id "unknown_t...  "HPP12_0618"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
2478         NaN      NaN       NaN         NaN     NaN  660930.A/C,G   

     CDS Start CDS End       Gene ID  \
2478    660696  661811  "HPP12_0618"   

                                        Product  
2478  "vacuolating cytotoxin VacA-like protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
2381  CP001217.1  Genbank  CDS  621145  624078  .  -  0   

                                                      8       gene_id  ...  \
2381  gene_id "HPP12_0593"; transcript_id "unknown_t...  "HPP12_0593"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
2381         NaN      NaN       NaN         NaN     NaN  621252.A/C,T   

     CDS Start CDS End       Gene ID                 Product  
2381    621145  624078  "HPP12_0593"  "hypothetical protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4397  CP001217.1  Genbank  CDS  1164069  1164623  .  -  0   

                                                      8       gene_id  ...  \
4397  gene_id "HPP12_1090"; transcript_id "unknown_t...  "HPP12_1090"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4397         NaN      NaN       NaN         NaN     NaN  1164536.C/T   

     CDS Start  CDS End       Gene ID  \
4397   1164069  1164623  "HPP12_1090"   

                                              Product  
4397  "fkbp-type peptidyl-prolyl cis-trans isomerase"  

[1 rows x 29 columns]
Empty DataFrame
Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, gene_id, gbkey, gene, gene_biotype, locus_tag, transcript_id, note, product, protein_id, transl_table, exon_number, probably, exception, interrupted, missing, Phenotype, CDS Start, CDS End, Gene ID, Product]
Index: []

[0 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
463  CP001217.1  Genbank  CDS  117857  118684  .  -  0   

                                                     8       gene_id  ...  \
463  gene_id "HPP12_0113"; transcript_id "unknown_t...  "HPP12_0113"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
463         NaN      NaN       NaN         NaN     NaN  117865.T/C    117857   

    CDS End       Gene ID                                            Product  
463  118684  "HPP12_0113"  "heat-inducible transcription repressor of cla...  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
471  CP001217.1  Genbank  CDS  119554  119847  .  +  0   

                                                     8       gene_id  ...  \
471  gene_id "HPP12_0115"; transcript_id "unknown_t...  "HPP12_0115"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
471         NaN      NaN       NaN         NaN     NaN  119782.G/A    119554   

    CDS End       Gene ID                 Product  
471  119847  "HPP12_0115"  "hypothetical protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4947  CP001217.1  Genbank  CDS  1311370  1311972  .  +  0   

                                                      8       gene_id  ...  \
4947  gene_id "HPP12_1223"; transcript_id "unknown_t...  "HPP12_1223"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4947         NaN      NaN       NaN         NaN     NaN  1311769.G/A,T   

     CDS Start  CDS End       Gene ID                              Product  
4947   1311370  1311972  "HPP12_1223"  "orotate phosphoribosyltransferase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
5015  CP001217.1  Genbank  CDS  1327209  1329611  .  +  0   

                                                      8       gene_id  ...  \
5015  gene_id "HPP12_1240"; transcript_id "unknown_t...  "HPP12_1240"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
5015         NaN      NaN       NaN         NaN     NaN  1327892.G/A,T   

     CDS Start  CDS End       Gene ID                       Product  
5015   1327209  1329611  "HPP12_1240"  "paralysed flagella protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3439  CP001217.1  Genbank  CDS  908318  909700  .  -  0   

                                                      8       gene_id  ...  \
3439  gene_id "HPP12_0858"; transcript_id "unknown_t...  "HPP12_0858"  ...   

     exon_number probably exception interrupted missing       Phenotype  \
3439         NaN      NaN       NaN         NaN     NaN  908604.T/A,C,G   

     CDS Start CDS End       Gene ID                 Product  
3439    908318  909700  "HPP12_0858"  "ADP-heptose synthase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
1325  CP001217.1  Genbank  CDS  342744  343547  .  +  0   

                                                      8       gene_id  ...  \
1325  gene_id "HPP12_0328"; transcript_id "unknown_t...  "HPP12_0328"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
1325         NaN      NaN       NaN         NaN     NaN  343251.G/A    342744   

     CDS End       Gene ID                                 Product  
1325  343547  "HPP12_0328"  "MinD cell division inhibitor protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
5183  CP001217.1  Genbank  CDS  1359113  1359757  .  -  0   

                                                      8       gene_id  ...  \
5183  gene_id "HPP12_1282"; transcript_id "unknown_t...  "HPP12_1282"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
5183         NaN      NaN       NaN         NaN     NaN  1359214.T/C   

     CDS Start  CDS End       Gene ID                     Product  
5183   1359113  1359757  "HPP12_1282"  "50S ribosomal protein L4"  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
475  CP001217.1  Genbank  CDS  119865  121745  .  -  0   

                                                     8       gene_id  ...  \
475  gene_id "HPP12_0116"; transcript_id "unknown_t...  "HPP12_0116"  ...   

    exon_number probably exception interrupted missing     Phenotype  \
475         NaN      NaN       NaN         NaN     NaN  120251.T/C,G   

    CDS Start CDS End       Gene ID                 Product  
475    119865  121745  "HPP12_0116"  "hypothetical protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3715  CP001217.1  Genbank  CDS  988669  989577  .  +  0   

                                                      8       gene_id  ...  \
3715  gene_id "HPP12_0926"; transcript_id "unknown_t...  "HPP12_0926"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
3715         NaN      NaN       NaN         NaN     NaN  989074.G/A,T   

     CDS Start CDS End       Gene ID                                 Product  
3715    988669  989577  "HPP12_0926"  "trans-isoprenyl diphosphate synthase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
1070  CP001217.1  Genbank  CDS  272839  273972  .  +  0   

                                                      8       gene_id  ...  \
1070  gene_id "HPP12_0265"; transcript_id "unknown_t...  "HPP12_0265"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
1070         NaN      NaN       NaN         NaN     NaN  273409.A/G    272839   

     CDS End       Gene ID           Product  
1070  273972  "HPP12_0265"  "dihydroorotase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
1230  CP001217.1  Genbank  CDS  320919  322208  .  +  0   

                                                      8       gene_id  ...  \
1230  gene_id "HPP12_0305"; transcript_id "unknown_t...  "HPP12_0305"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
1230         NaN      NaN       NaN         NaN     NaN  321592.C/T    320919   

     CDS End       Gene ID                                     Product  
1230  322208  "HPP12_0305"  "glutamate-1-semialdehyde 2,1-aminomutase"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3587  CP001217.1  Genbank  CDS  947918  949027  .  -  0   

                                                      8       gene_id  ...  \
3587  gene_id "HPP12_0894"; transcript_id "unknown_t...  "HPP12_0894"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
3587         NaN      NaN       NaN         NaN     NaN  948472.T/C,G   

     CDS Start CDS End       Gene ID  \
3587    947918  949027  "HPP12_0894"   

                                              Product  
3587  "hydrogenase expression/formation protein HypD"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4991  CP001217.1  Genbank  CDS  1320869  1321528  .  +  0   

                                                      8       gene_id  ...  \
4991  gene_id "HPP12_1234"; transcript_id "unknown_t...  "HPP12_1234"  ...   

     exon_number probably exception interrupted missing        Phenotype  \
4991         NaN      NaN       NaN         NaN     NaN  1320944.G/A,C,T   

     CDS Start  CDS End       Gene ID  \
4991   1320869  1321528  "HPP12_1234"   

                                       Product  
4991  "NADH-ubiquinone oxidoreductase chain I"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4257  CP001217.1  Genbank  CDS  1126116  1127138  .  +  0   

                                                      8       gene_id  ...  \
4257  gene_id "HPP12_1056"; transcript_id "unknown_t...  "HPP12_1056"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4257         NaN      NaN       NaN         NaN     NaN  1126392.G/A   

     CDS Start  CDS End       Gene ID  \
4257   1126116  1127138  "HPP12_1056"   

                                                Product  
4257  "ribonucleoside-diphosphate reductase 1 beta s...  

[1 rows x 29 columns]
              0        1    2       3       4  5  6  7  \
967  CP001217.1  Genbank  CDS  246351  247697  .  -  0   

                                                     8       gene_id  ...  \
967  gene_id "HPP12_0239"; transcript_id "unknown_t...  "HPP12_0239"  ...   

    exon_number probably exception interrupted missing   Phenotype CDS Start  \
967         NaN      NaN       NaN         NaN     NaN  246770.C/T    246351   

    CDS End       Gene ID                    Product  
967  247697  "HPP12_0239"  "glutamyl-tRNA reductase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4931  CP001217.1  Genbank  CDS  1308295  1309272  .  -  0   

                                                      8       gene_id  ...  \
4931  gene_id "HPP12_1219"; transcript_id "unknown_t...  "HPP12_1219"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4931         NaN      NaN       NaN         NaN     NaN  1308332.G/A   

     CDS Start  CDS End       Gene ID                         Product  
4931   1308295  1309272  "HPP12_1219"  "tryptophanyl-tRNA synthetase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
3807  CP001217.1  Genbank  CDS  1008368  1008979  .  +  0   

                                                      8       gene_id  ...  \
3807  gene_id "HPP12_0947"; transcript_id "unknown_t...  "HPP12_0947"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
3807         NaN      NaN       NaN         NaN     NaN  1008816.A/G   

     CDS Start  CDS End       Gene ID                         Product  
3807   1008368  1008979  "HPP12_0947"  "acyl-coa thioester hydrolase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4329  CP001217.1  Genbank  CDS  1145115  1145672  .  +  0   

                                                      8       gene_id  ...  \
4329  gene_id "HPP12_1073"; transcript_id "unknown_t...  "HPP12_1073"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4329         NaN      NaN       NaN         NaN     NaN  1145413.A/G   

     CDS Start  CDS End       Gene ID  \
4329   1145115  1145672  "HPP12_1073"   

                                                Product  
4329  "pyruvate ferredoxin oxidoreductase gamma subu...  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4421  CP001217.1  Genbank  CDS  1168781  1169347  .  -  0   

                                                      8       gene_id  ...  \
4421  gene_id "HPP12_1096"; transcript_id "unknown_t...  "HPP12_1096"  ...   

     exon_number probably exception interrupted missing      Phenotype  \
4421         NaN      NaN       NaN         NaN     NaN  1169338.A/C,G   

     CDS Start  CDS End       Gene ID  \
4421   1168781  1169347  "HPP12_1096"   

                                            Product  
4421  "biopolymer transport accessory protein ExbB"  

[1 rows x 29 columns]
              0        1    2      3      4  5  6  7  \
147  CP001217.1  Genbank  CDS  37520  38260  .  +  0   

                                                     8       gene_id  ...  \
147  gene_id "HPP12_0035"; transcript_id "unknown_t...  "HPP12_0035"  ...   

    exon_number probably exception interrupted missing  Phenotype CDS Start  \
147         NaN      NaN       NaN         NaN     NaN  38255.A/G     37520   

    CDS End       Gene ID                     Product  
147   38260  "HPP12_0035"  "ComB8 competence protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
1186  CP001217.1  Genbank  CDS  309196  311679  .  -  0   

                                                      8       gene_id  ...  \
1186  gene_id "HPP12_0294"; transcript_id "unknown_t...  "HPP12_0294"  ...   

     exon_number probably exception interrupted missing       Phenotype  \
1186         NaN      NaN       NaN         NaN     NaN  310664.G/A,C,T   

     CDS Start CDS End       Gene ID                                Product  
1186    309196  311679  "HPP12_0294"  "flagellar-hook associated protein 3"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
5627  CP001217.1  Genbank  CDS  1475140  1477899  .  -  0   

                                                      8       gene_id  ...  \
5627  gene_id "HPP12_1395"; transcript_id "unknown_t...  "HPP12_1395"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
5627         NaN      NaN       NaN         NaN     NaN  1477665.C/T   

     CDS Start  CDS End       Gene ID                      Product  
5627   1475140  1477899  "HPP12_1395"  "isoleucyl-tRNA synthetase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4203  CP001217.1  Genbank  CDS  1111794  1114601  .  -  0   

                                                      8       gene_id  ...  \
4203  gene_id "HPP12_1043"; transcript_id "unknown_t...  "HPP12_1043"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4203         NaN      NaN       NaN         NaN     NaN  1113403.T/C   

     CDS Start  CDS End       Gene ID                                 Product  
4203   1111794  1114601  "HPP12_1043"  "cytochrome C-type biogenesis protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3153  CP001217.1  Genbank  CDS  837458  838138  .  -  0   

                                                      8       gene_id  ...  \
3153  gene_id "HPP12_0787"; transcript_id "unknown_t...  "HPP12_0787"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
3153         NaN      NaN       NaN         NaN     NaN  837880.T/C,G   

     CDS Start CDS End       Gene ID                 Product  
3153    837458  838138  "HPP12_0787"  "hypothetical protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
1977  CP001217.1  Genbank  CDS  513770  514711  .  -  0   

                                                      8       gene_id  ...  \
1977  gene_id "HPP12_0492"; transcript_id "unknown_t...  "HPP12_0492"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
1977         NaN      NaN       NaN         NaN     NaN  514285.C/A,T   

     CDS Start CDS End       Gene ID                  Product  
1977    513770  514711  "HPP12_0492"  "catalase like protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4269  CP001217.1  Genbank  CDS  1128830  1129555  .  +  0   

                                                      8       gene_id  ...  \
4269  gene_id "HPP12_1059"; transcript_id "unknown_t...  "HPP12_1059"  ...   

     exon_number probably exception interrupted missing        Phenotype  \
4269         NaN      NaN       NaN         NaN     NaN  1128957.C/A,G,T   

     CDS Start  CDS End       Gene ID                       Product  
4269   1128830  1129555  "HPP12_1059"  "pseudouridylate synthase I"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3185  CP001217.1  Genbank  CDS  848497  849726  .  +  0   

                                                      8       gene_id  ...  \
3185  gene_id "HPP12_0795"; transcript_id "unknown_t...  "HPP12_0795"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
3185         NaN      NaN       NaN         NaN     NaN  848527.A/G    848497   

     CDS End       Gene ID                                            Product  
3185  849726  "HPP12_0795"  "lipoprotein release system transmembrane prot...  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4297  CP001217.1  Genbank  CDS  1135395  1137218  .  -  0   

                                                      8       gene_id  ...  \
4297  gene_id "HPP12_1065"; transcript_id "unknown_t...  "HPP12_1065"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4297         NaN      NaN       NaN         NaN     NaN  1135539.T/G   

     CDS Start  CDS End       Gene ID                           Product  
4297   1135395  1137218  "HPP12_1065"  "6-phosphogluconate dehydratase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
3891  CP001217.1  Genbank  CDS  1029242  1031344  .  -  0   

                                                      8       gene_id  ...  \
3891  gene_id "HPP12_0968"; transcript_id "unknown_t...  "HPP12_0968"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
3891         NaN      NaN       NaN         NaN     NaN  1031026.G/T   

     CDS Start  CDS End       Gene ID                                Product  
3891   1029242  1031344  "HPP12_0968"  "glycyl-tRNA synthetase beta subunit"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
5591  CP001217.1  Genbank  CDS  1467670  1468008  .  +  0   

                                                      8       gene_id  ...  \
5591  gene_id "HPP12_1386"; transcript_id "unknown_t...  "HPP12_1386"  ...   

     exon_number probably exception interrupted missing        Phenotype  \
5591         NaN      NaN       NaN         NaN     NaN  1467994.C/A,G,T   

     CDS Start  CDS End       Gene ID                 Product  
5591   1467670  1468008  "HPP12_1386"  "hypothetical protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
3121  CP001217.1  Genbank  CDS  828445  829518  .  +  0   

                                                      8       gene_id  ...  \
3121  gene_id "HPP12_0779"; transcript_id "unknown_t...  "HPP12_0779"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
3121         NaN      NaN       NaN         NaN     NaN  828718.G/A,C   

     CDS Start CDS End       Gene ID                           Product  
3121    828445  829518  "HPP12_0779"  "flagellar biosynthetic protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
3883  CP001217.1  Genbank  CDS  1026881  1027957  .  -  0   

                                                      8       gene_id  ...  \
3883  gene_id "HPP12_0966"; transcript_id "unknown_t...  "HPP12_0966"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
3883         NaN      NaN       NaN         NaN     NaN  1027216.C/T   

     CDS Start  CDS End       Gene ID  \
3883   1026881  1027957  "HPP12_0966"   

                                       Product  
3883  "cobalt-zinc-cadmium resistance protein"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
4883  CP001217.1  Genbank  CDS  1293546  1296086  .  -  0   

                                                      8       gene_id  ...  \
4883  gene_id "HPP12_1207"; transcript_id "unknown_t...  "HPP12_1207"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
4883         NaN      NaN       NaN         NaN     NaN  1293845.T/C   

     CDS Start  CDS End       Gene ID                   Product  
4883   1293546  1296086  "HPP12_1207"  "alanyl-tRNA synthetase"  

[1 rows x 29 columns]
               0        1    2        3        4  5  6  7  \
5059  CP001217.1  Genbank  CDS  1341103  1341837  .  -  0   

                                                      8       gene_id  ...  \
5059  gene_id "HPP12_1251"; transcript_id "unknown_t...  "HPP12_1251"  ...   

     exon_number probably exception interrupted missing    Phenotype  \
5059         NaN      NaN       NaN         NaN     NaN  1341574.C/T   

     CDS Start  CDS End       Gene ID                         Product  
5059   1341103  1341837  "HPP12_1251"  "acid phosphatase lipoprotein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
2542  CP001217.1  Genbank  CDS  673942  676191  .  -  0   

                                                      8       gene_id  ...  \
2542  gene_id "HPP12_0634"; transcript_id "unknown_t...  "HPP12_0634"  ...   

     exon_number probably exception interrupted missing   Phenotype CDS Start  \
2542         NaN      NaN       NaN         NaN     NaN  676016.G/A    673942   

     CDS End       Gene ID                        Product  
2542  676191  "HPP12_0634"  "DNA mismatch repair protein"  

[1 rows x 29 columns]
               0        1    2       3       4  5  6  7  \
1198  CP001217.1  Genbank  CDS  312634  314283  .  +  0   

                                                      8       gene_id  ...  \
1198  gene_id "HPP12_0297"; transcript_id "unknown_t...  "HPP12_0297"  ...   

     exon_number probably exception interrupted missing     Phenotype  \
1198         NaN      NaN       NaN         NaN     NaN  314047.G/A,C   

     CDS Start CDS End       Gene ID                                  Product  
1198    312634  314283  "HPP12_0297"  "periplasmic dipeptide-binding protein"  

[1 rows x 29 columns]


len(temp_gtf_final)

274


temp_gtf_final['gene']

872     "cdsA"
3651    "alpA"
4207       NaN
2678       NaN
3439    "waaE"
         ...  
3883       NaN
4883    "alaS"
5059       NaN
2542    "mutS"
1198    "dppA"
Name: gene, Length: 136, dtype: object


info_final_all3 = temp_gtf_final[['Phenotype','CDS Start','CDS End',"Gene ID","Product",'gene']]


info_final_all3


info_final_all3 = info_final_all3.reset_index()
del info_final_all3['index']


info_final_all3


info_final_all3.to_csv("print.csv")


info_final_all3


import shap


explainer = shap.TreeExplainer(ENrfRET)
shap_values = explainer.shap_values(dataTN[[top_varaibles[x] for x in selected_features]])


shap.summary_plot(shap_values, dataTN[[top_varaibles[x] for x in selected_features]])


df_mnlk = pd.DataFrame()
df_mnlk.loc[0,'Top 10 SHAP Features']='221331.T/C'
df_mnlk.loc[1,'Top 10 SHAP Features']='908373.T/C'
df_mnlk.loc[2,'Top 10 SHAP Features']='962858.G/A'
df_mnlk.loc[3,'Top 10 SHAP Features']='1009179.C/A,T'
df_mnlk.loc[4,'Top 10 SHAP Features']='1181754.C/A,T'
df_mnlk.loc[5,'Top 10 SHAP Features']='828717.T/A,C,G'
df_mnlk.loc[6,'Top 10 SHAP Features']='310663.A/G,T'
df_mnlk.loc[7,'Top 10 SHAP Features']='1115013.G/A,T'
df_mnlk.loc[8,'Top 10 SHAP Features']='712033.A/C,G,T'
df_mnlk.loc[9,'Top 10 SHAP Features']='1140792.G/A'


df_mnlk


feature_impDF1[:10]


info_final_all3


new_df_vcf_phen101 = pd.DataFrame()
for i in range(len(feature_impDF1['Features'])):
    new =  info_final_all3[info_final_all3['Phenotype']==list(feature_impDF1['Features'])[i]]
    new_df_vcf_phen101 = pd.concat([new_df_vcf_phen101, new], axis=0)


new_df_vcf_phen101 = new_df_vcf_phen101.reset_index()
del new_df_vcf_phen101['index']


new_df_vcf_phen101.to_csv('print.csv')


shap.summary_plot(shap_values[0], dataTN[[top_varaibles[x] for x in selected_features]])


shap.summary_plot(shap_values[1], dataTN[[top_varaibles[x] for x in selected_features]])
len(shap_values[1])

714


shap.summary_plot(shap_values[2], dataTN[[top_varaibles[x] for x in selected_features]])


vals= np.abs(shap_values[0]).mean(0)
feature_importance = pd.DataFrame(list(zip(dataTN[[top_varaibles[x] for x in selected_features]].columns,vals)),
                                  columns=['Top  10 SHAP Features for detecting Gastric Cancer','feature_importance_vals'])
feature_importance.sort_values(by=['feature_importance_vals'],ascending=False,inplace=True)
feature_importance[:10]


vals= np.abs(shap_values[1]).mean(0)
feature_importance = pd.DataFrame(list(zip(dataTN[[top_varaibles[x] for x in selected_features]].columns,vals)),
                                  columns=['col_name','feature_importance_vals'])
feature_importance.sort_values(by=['feature_importance_vals'],ascending=False,inplace=True)
feature_importance[:20]


vals= np.abs(shap_values[2]).mean(0)
feature_importance = pd.DataFrame(list(zip(dataTN[[top_varaibles[x] for x in selected_features]].columns,vals)),columns=['col_name','feature_importance_vals'])
feature_importance.sort_values(by=['feature_importance_vals'],ascending=False,inplace=True)
feature_importance[:20]


shap.summary_plot(shap_values, dataTN[[top_varaibles[x] for x in selected_features]])


feature_impDF2 = feature_impDF1.copy()
feature_impDF2['Top 10 VIM Features']=feature_impDF2['Features']
del feature_impDF2['Importance']
del feature_impDF2['Features']
del feature_impDF2['Top 10 VIM']


feature_impDF2[0:10]


print(feature_impDF1[0:10]['Top 10 VIM Features'])

0     712033.A/C,G,T
1         221331.T/C
2       310663.A/G,T
3     828717.T/A,C,G
4         908373.T/C
5      1115013.G/A,T
6      1181754.C/A,T
7         962858.G/A
8    1131910.G/A,C,T
9      1009179.C/A,T
Name: Top 10 VIM, dtype: object


shap.initjs()
shap.force_plot(explainer.expected_value[1], shap_values[1], dataTN[[top_varaibles[x] for x in selected_features]])


shap_values = explainer.shap_values(ENRtest_featuresT)
shap.force_plot(explainer.expected_value[0], shap_values[0], ENRtest_featuresT)


shap.summary_plot(shap_values[0], dataTN[[top_varaibles[x] for x in selected_features]])


y_train = upsampled.Phenotype
X_train = upsampled.drop('Phenotype', axis=1)

undersampled = RandomForestClassifier(n_estimators = 1000,max_depth= 20,random_state = 42).fit(X_train, y_train)

undersampled_pred = undersampled.predict(X_test)


print(classification_report(y_test, undersampled_pred))

                            precision    recall  f1-score   support

            Gastric Cancer       0.46      0.54      0.50        24
    Non Atrophic Gastritis       0.76      0.54      0.63       114
Progressive towards Cancer       0.37      0.63      0.46        41

                  accuracy                           0.56       179
                 macro avg       0.53      0.57      0.53       179
              weighted avg       0.63      0.56      0.57       179


from sklearn import metrics

def compute_roc_auc(index):
    y_predict = ENrfRET.predict_proba(dataTN[[top_varaibles[x] for x in selected_features]].iloc[index])[:,1]
    fpr, tpr, thresholds = metrics.roc_curve(dataTN['Phenotype'].iloc[index], y_predict)
    auc_score = auc(fpr, tpr)
    return fpr, tpr, auc_score


from sklearn.model_selection import StratifiedKFold
from sklearn import metrics

cv = StratifiedKFold(n_splits=2, random_state=123, shuffle=True)
results = pd.DataFrame(columns=['training_score', 'test_score'])
fprs, tprs, scores = [], [], []
    
for (train, test), i in zip(cv.split(dataTN[[top_varaibles[x] for x in selected_features]], dataTN['Phenotype']), range(2)):
    ENrfRET.fit(dataTN[[top_varaibles[x] for x in selected_features]].iloc[train], dataTN['Phenotype'].iloc[train])
    prediction = ENrfRET.predict(dataTN[[top_varaibles[x] for x in selected_features]].iloc[test])
    print(i+1,"Accuracy:",metrics.accuracy_score(dataTN['Phenotype'].iloc[test], prediction))    
    print(metrics.classification_report(dataTN['Phenotype'].iloc[test], prediction))
    print("----------------------------------------")

1 Accuracy: 0.7366946778711485
                            precision    recall  f1-score   support

            Gastric Cancer       1.00      0.25      0.40        48
    Non Atrophic Gastritis       0.72      0.92      0.81       208
Progressive towards Cancer       0.74      0.59      0.66       101

                  accuracy                           0.74       357
                 macro avg       0.82      0.59      0.62       357
              weighted avg       0.77      0.74      0.71       357

----------------------------------------
2 Accuracy: 0.742296918767507
                            precision    recall  f1-score   support

            Gastric Cancer       0.82      0.29      0.43        48
    Non Atrophic Gastritis       0.72      0.94      0.82       208
Progressive towards Cancer       0.79      0.55      0.65       101

                  accuracy                           0.74       357
                 macro avg       0.78      0.59      0.63       357
              weighted avg       0.76      0.74      0.72       357

----------------------------------------


for index, row in final_variable_df.iterrows():
    
    if final_variable_df.loc[index, 1]>= read_variant_dict_gtf[3]:
        if final_variable_df.loc[index, 1]<= read_variant_dict_gtf[4]:
            
    final_variable_df.loc[index, "gene" ]= read_variant_dict_gtf[index_i, gene]


if read_variant_dict_gtf[final_variable_df.loc[1, 1]>= read_variant_dict_gtf[3]][3]:
    if final_variable_df.loc[1, 1]<= read_variant_dict_gtf[4]:
        print (read_variant_dict_gtf[1, "gene_name"])

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-666-759323b3d43a> in <module>
----> 1 if final_variable_df.loc[1, 1]>= read_variant_dict_gtf[3]:
      2     if final_variable_df.loc[1, 1]<= read_variant_dict_gtf[4]:
      3         print (read_variant_dict_gtf[1, "gene_name"])

~/miniconda3/lib/python3.9/site-packages/pandas/core/generic.py in __nonzero__(self)
   1440     @final
   1441     def __nonzero__(self):
-> 1442         raise ValueError(
   1443             f"The truth value of a {type(self).__name__} is ambiguous. "
   1444             "Use a.empty, a.bool(), a.item(), a.any() or a.all()."

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().


read_variant_dict_gtf[final_variable_df.loc[1, 1]>= read_variant_dict_gtf[3]][3]

0           13
8          911
16        2515
20        3199
24        4043
         ...  
3622    952453
3630    955244
3634    956260
3646    959872
3650    962720
Name: 3, Length: 451, dtype: int64


import pandas as pd
X = pd.read_pickle(r'../bacgwasim/recomb_0.2/results_BacGWASim/simulations/genSim/sims.pickle')


X
#/genSim/sims.pickle')


#X['ID']=X.index
X = X.reset_index()


del X["index"]


Y=pd.read_csv(r'../bacgwasim/recomb_0.2/results_BacGWASim/simulations/phenSim/0/phenSim.phen',' ',header=None)

Y


mn = pd.DataFrame(Y[2])
#X = pd.concat([X,mn], axis=1)
#X.rename(columns = {2:'Phenotype'}, inplace = True)


# Using Skicit-learn to split data into training and testing sets
from sklearn.model_selection import train_test_split

# Split the data into training and testing sets
train_features, test_features, train_labels, test_labels = train_test_split(X, Y[2], test_size = 0.25, random_state = 42)


print('Training Features Shape:', train_features.shape)
print('Training Labels Shape:', train_labels.shape)
print('Testing Features Shape:', test_features.shape)
print('Testing Labels Shape:', test_labels.shape)

Training Features Shape: (600, 287147)
Training Labels Shape: (600,)
Testing Features Shape: (200, 287147)
Testing Labels Shape: (200,)


import numpy as np
train_labels.shape

(600,)


from sklearn.ensemble import RandomForestRegressor
# Instantiate model with 1000 decision trees
rf = RandomForestClassifier(n_estimators = 1000, random_state = 42)
# Train the model on training data


rf.fit(train_features, train_labels);


# Use the forest's predict method on the test data
predictions = rf.predict(test_features)
# Calculate the absolute errors
errors = abs(predictions - test_labels)
# Print out the mean absolute error (mae)
print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')
# Model Accuracy, how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(test_labels, predictions))
print("Accuracy:",metrics.f1_score(test_labels, predictions,average="macro"))
print("Accuracy:",metrics.recall_score(test_labels, predictions,average="macro"))

Mean Absolute Error: 0.08 degrees.
Accuracy: 0.92
Accuracy: 0.7871774408087258
Accuracy: 0.7485714285714286


print(classification_report(test_labels, predictions))

              precision    recall  f1-score   support

           1       0.93      0.98      0.96       175
           2       0.76      0.52      0.62        25

    accuracy                           0.92       200
   macro avg       0.85      0.75      0.79       200
weighted avg       0.91      0.92      0.91       200


feat_selector = BorutaPy(rf, n_estimators ='auto', verbose=2, random_state=1,max_iter = 50)

#ENrfRET.fit(ENRtrain_featuresT,ENRtrain_labelsT)
# find all relevant features - 5 features should be selected
feat_selector.fit(train_features.values, train_labels.values.ravel())

# check selected features - first 5 features are selected
feat_selector.support_

# check ranking of features
feat_selector.ranking_

# call transform() on X to filter it down to selected features
X_filtered = feat_selector.transform(train_features.values)

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-342-2ae082e5c5e3> in <module>
      3 #ENrfRET.fit(ENRtrain_featuresT,ENRtrain_labelsT)
      4 # find all relevant features - 5 features should be selected
----> 5 feat_selector.fit(train_features.values, train_labels.values.ravel())
      6 
      7 # check selected features - first 5 features are selected

~/miniconda3/lib/python3.9/site-packages/boruta/boruta_py.py in fit(self, X, y)
    199         """
    200 
--> 201         return self._fit(X, y)
    202 
    203     def transform(self, X, weak=False):

~/miniconda3/lib/python3.9/site-packages/boruta/boruta_py.py in _fit(self, X, y)
    283 
    284             # add shadow attributes, shuffle them and train estimator, get imps
--> 285             cur_imp = self._add_shadows_get_imps(X, y, dec_reg)
    286 
    287             # get the threshold of shadow importances we will use for rejection

~/miniconda3/lib/python3.9/site-packages/boruta/boruta_py.py in _add_shadows_get_imps(self, X, y, dec_reg)
    410         x_sha = np.apply_along_axis(self._get_shuffle, 0, x_sha)
    411         # get importance of the merged matrix
--> 412         imp = self._get_imp(np.hstack((x_cur, x_sha)), y)
    413         # separate importances of real and shadow features
    414         imp_sha = imp[x_cur_w:]

~/miniconda3/lib/python3.9/site-packages/boruta/boruta_py.py in _get_imp(self, X, y)
    382     def _get_imp(self, X, y):
    383         try:
--> 384             self.estimator.fit(X, y)
    385         except Exception as e:
    386             raise ValueError('Please check your X and y variable. The provided'

~/miniconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py in fit(self, X, y, sample_weight)
    385             # parallel_backend contexts set at a higher level,
    386             # since correctness does not rely on using threads.
--> 387             trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
    388                              **_joblib_parallel_args(prefer='threads'))(
    389                 delayed(_parallel_build_trees)(

~/miniconda3/lib/python3.9/site-packages/joblib/parallel.py in __call__(self, iterable)
   1042                 self._iterating = self._original_iterator is not None
   1043 
-> 1044             while self.dispatch_one_batch(iterator):
   1045                 pass
   1046 

~/miniconda3/lib/python3.9/site-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
    857                 return False
    858             else:
--> 859                 self._dispatch(tasks)
    860                 return True
    861 

~/miniconda3/lib/python3.9/site-packages/joblib/parallel.py in _dispatch(self, batch)
    775         with self._lock:
    776             job_idx = len(self._jobs)
--> 777             job = self._backend.apply_async(batch, callback=cb)
    778             # A job can complete so quickly than its callback is
    779             # called before we get here, causing self._jobs to

~/miniconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py in apply_async(self, func, callback)
    206     def apply_async(self, func, callback=None):
    207         """Schedule a func to be run"""
--> 208         result = ImmediateResult(func)
    209         if callback:
    210             callback(result)

~/miniconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py in __init__(self, batch)
    570         # Don't delay the application, to avoid keeping the input
    571         # arguments in memory
--> 572         self.results = batch()
    573 
    574     def get(self):

~/miniconda3/lib/python3.9/site-packages/joblib/parallel.py in __call__(self)
    260         # change the default number of processes to -1
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262             return [func(*args, **kwargs)
    263                     for func, args, kwargs in self.items]
    264 

~/miniconda3/lib/python3.9/site-packages/joblib/parallel.py in <listcomp>(.0)
    260         # change the default number of processes to -1
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262             return [func(*args, **kwargs)
    263                     for func, args, kwargs in self.items]
    264 

~/miniconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py in __call__(self, *args, **kwargs)
    220     def __call__(self, *args, **kwargs):
    221         with config_context(**self.config):
--> 222             return self.function(*args, **kwargs)

~/miniconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py in _parallel_build_trees(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)
    167                                                         indices=indices)
    168 
--> 169         tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
    170     else:
    171         tree.fit(X, y, sample_weight=sample_weight, check_input=False)

~/miniconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    901         """
    902 
--> 903         super().fit(
    904             X, y,
    905             sample_weight=sample_weight,

~/miniconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    392                                            min_impurity_split)
    393 
--> 394         builder.build(self.tree_, X, y, sample_weight)
    395 
    396         if self.n_outputs_ == 1 and is_classifier(self):

KeyboardInterrupt:


import pandas as pd
feature_imp = pd.Series(rf.feature_importances_,index=X.iloc[:,:].columns).sort_values(ascending=False)


feature_impDF = pd.DataFrame({'Features':feature_imp.index, 'Importance':feature_imp.values})
#top_varaibles = list(feature_impDF['Features'][feature_impDF["Importance"]>0.0001])


feature_impDF


##### import matplotlib.pyplot as plt
import seaborn as sns


# seaborn histogram

sns.distplot(feature_impDF["Importance"], hist=True, kde=False, 
             bins=200, color = 'blue',
             hist_kws={'edgecolor':'black'}).set(xlabel="VIM",ylabel='Number of Variables',title='Distribution of Variables ')

/home/vbha0006/miniconda3/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

[Text(0.5, 0, 'VIM'),
 Text(0, 0.5, 'Number of Variables'),
 Text(0.5, 1.0, 'Distribution of Variables ')]


##### import matplotlib.pyplot as plt
import seaborn as sns


# seaborn histogram

sns.distplot(list(feature_impDF["Importance"][feature_impDF["Importance"]>0.00002])
, hist=True, kde=False, 
             bins=200, color = 'blue',
             hist_kws={'edgecolor':'black'}).set(xlabel="VIM",ylabel='Number of Variables',title='Distribution of Variables ')

/home/vbha0006/miniconda3/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

[Text(0.5, 0, 'VIM'),
 Text(0, 0.5, 'Number of Variables'),
 Text(0.5, 1.0, 'Distribution of Variables ')]


rf = RandomForestClassifier(n_estimators = 1000,random_state = 42)
info_dfk=pd.DataFrame()
for i in range(1,900,+10):
    vim_temp = 0.0000001*i
    top_varaiblesTesting = list(feature_impDF['Features'][feature_impDF["Importance"]>=vim_temp])
    
    #ENXnRe = np.array(dataTN[top_varaiblesTesting])


# Split the data into training and testing sets
    ENRtrain_features, ENRtest_features, ENRtrain_labels, ENRtest_labels = train_test_split(X[top_varaiblesTesting], Y[2], test_size = 0.25, random_state = 42)

    rf.fit(ENRtrain_features,ENRtrain_labels)

    ENRprediction=rf.predict(ENRtest_features)
    ENRprediction1=rf.predict(ENRtrain_features)

    mn= pd.DataFrame(columns = ['vim above or equal','accuracy'])
    mn.loc[0,'vim above or equal']=vim_temp
    mn.loc[0,'Test Accuracy']= metrics.accuracy_score(ENRtest_labels, ENRprediction)
    mn.loc[0,'Train Accuracy']= metrics.accuracy_score(ENRtrain_labels, ENRprediction1)

    mn.loc[0,'Number of Variables selected'] = len(top_varaiblesTesting)
    print(mn)
    info_dfk = pd.concat([info_dfk,mn],axis=0)

  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0                0.0      NaN           0.92             1.0   

   Number of Variables selected  
0                       26898.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0           0.000001      NaN           0.92             1.0   

   Number of Variables selected  
0                       26709.0  
  vim above or equal accuracy  Test Accuracy  Train Accuracy  \
0           0.000002      NaN           0.92             1.0   

   Number of Variables selected  
0                       26277.0

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-319-f1a8b373842b> in <module>
     11     ENRtrain_features, ENRtest_features, ENRtrain_labels, ENRtest_labels = train_test_split(X[top_varaiblesTesting], Y[2], test_size = 0.25, random_state = 42)
     12 
---> 13     rf.fit(ENRtrain_features,ENRtrain_labels)
     14 
     15     ENRprediction=rf.predict(ENRtest_features)

~/miniconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py in fit(self, X, y, sample_weight)
    385             # parallel_backend contexts set at a higher level,
    386             # since correctness does not rely on using threads.
--> 387             trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
    388                              **_joblib_parallel_args(prefer='threads'))(
    389                 delayed(_parallel_build_trees)(

~/miniconda3/lib/python3.9/site-packages/joblib/parallel.py in __call__(self, iterable)
   1042                 self._iterating = self._original_iterator is not None
   1043 
-> 1044             while self.dispatch_one_batch(iterator):
   1045                 pass
   1046 

~/miniconda3/lib/python3.9/site-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
    857                 return False
    858             else:
--> 859                 self._dispatch(tasks)
    860                 return True
    861 

~/miniconda3/lib/python3.9/site-packages/joblib/parallel.py in _dispatch(self, batch)
    775         with self._lock:
    776             job_idx = len(self._jobs)
--> 777             job = self._backend.apply_async(batch, callback=cb)
    778             # A job can complete so quickly than its callback is
    779             # called before we get here, causing self._jobs to

~/miniconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py in apply_async(self, func, callback)
    206     def apply_async(self, func, callback=None):
    207         """Schedule a func to be run"""
--> 208         result = ImmediateResult(func)
    209         if callback:
    210             callback(result)

~/miniconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py in __init__(self, batch)
    570         # Don't delay the application, to avoid keeping the input
    571         # arguments in memory
--> 572         self.results = batch()
    573 
    574     def get(self):

~/miniconda3/lib/python3.9/site-packages/joblib/parallel.py in __call__(self)
    260         # change the default number of processes to -1
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262             return [func(*args, **kwargs)
    263                     for func, args, kwargs in self.items]
    264 

~/miniconda3/lib/python3.9/site-packages/joblib/parallel.py in <listcomp>(.0)
    260         # change the default number of processes to -1
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262             return [func(*args, **kwargs)
    263                     for func, args, kwargs in self.items]
    264 

~/miniconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py in __call__(self, *args, **kwargs)
    220     def __call__(self, *args, **kwargs):
    221         with config_context(**self.config):
--> 222             return self.function(*args, **kwargs)

~/miniconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py in _parallel_build_trees(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)
    167                                                         indices=indices)
    168 
--> 169         tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
    170     else:
    171         tree.fit(X, y, sample_weight=sample_weight, check_input=False)

~/miniconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    901         """
    902 
--> 903         super().fit(
    904             X, y,
    905             sample_weight=sample_weight,

~/miniconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    392                                            min_impurity_split)
    393 
--> 394         builder.build(self.tree_, X, y, sample_weight)
    395 
    396         if self.n_outputs_ == 1 and is_classifier(self):

KeyboardInterrupt:


import seaborn as sns
fig, ax = plt.subplots()
sns.lineplot(data=info_dfk, y="Train Accuracy", x="Number of Variables selected", label='Train',
               ).set(title='Accuracy of the Model with VIM above or equal to a specific value')
sns.lineplot(data=info_dfk, y="Test Accuracy", x="Number of Variables selected",label='Test', 
             color='r' ).set(title='Accuracy of the Model with varying number of Variables.',ylabel='Acurracy')
plt.show()


fig, ax = plt.subplots()
sns.lineplot(data=info_dfk, y="Train Accuracy", x="vim above or equal", label='Train',
               ).set(title='Accuracy of the Model with VIM above or equal to a specific value')
sns.lineplot(data=info_dfk, y="Test Accuracy", x="vim above or equal",label='Test', 
             color='r' ).set(title='Accuracy of the Model with VIM above or equal to a specific value.',ylabel='Acurracy')

plt.show()


feature_impDF["Importance"]

0         0.000914
1         0.000721
2         0.000620
3         0.000585
4         0.000566
            ...   
287142    0.000000
287143    0.000000
287144    0.000000
287145    0.000000
287146    0.000000
Name: Importance, Length: 287147, dtype: float64


top_varaibles = list(feature_impDF['Features'][feature_impDF["Importance"]>0.00009])
len(top_varaibles)

2132


#max_features="auto",criterion='entropy',max_depth=20,n_estimators=1000,random_state = 42
# Using Skicit-learn to split data into training and testing sets
from sklearn.model_selection import train_test_split
import numpy as np

ENXnRet = np.array(X[top_varaiblesTesting])


# Split the data into training and testing sets
ENRtrain_featuresT, ENRtest_featuresT, ENRtrain_labelsT, ENRtest_labelsT = train_test_split(X[top_varaibles], 
                                                                                            Y[2], 
                                                                                            test_size = 0.25, 
                                                                                            random_state = 42)

from sklearn.ensemble import RandomForestClassifier
# Instantiate model with 1000 decision trees
rf = RandomForestClassifier(n_estimators = 1000,random_state = 42)
                             #, min_samples_split=5,max_terminal_nodes=10, min_samples_leaf=200,random_state = 42)
# Train the model on training data

rf.fit(ENRtrain_featuresT,ENRtrain_labelsT)

ENRpredictionT=rf.predict(ENRtest_featuresT)

#Import scikit-learn metrics module for accuracy calculation
from sklearn import metrics
# Model Accuracy, how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(ENRtest_labelsT, ENRpredictionT))
print("Accuracy:",metrics.f1_score(ENRtest_labelsT, ENRpredictionT,average="macro"))
print("Accuracy:",metrics.recall_score(ENRtest_labelsT, ENRpredictionT,average="macro"))

Accuracy: 0.92
Accuracy: 0.7871774408087258
Accuracy: 0.7485714285714286


feat_mine = BorutaPy(rf, n_estimators ='auto', verbose=2, random_state=1,max_iter = 50)


feat_mine.fit(ENRtrain_featuresT.values,ENRtrain_labelsT.values.ravel())

Iteration: 	1 / 50
Confirmed: 	0
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Tentative: 	756
Rejected: 	1376
Iteration: 	9 / 50
Confirmed: 	50
Tentative: 	706
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Confirmed: 	64
Tentative: 	484
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Tentative: 	401
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Confirmed: 	81
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Confirmed: 	92
Tentative: 	279
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Tentative: 	268
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Confirmed: 	99
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Confirmed: 	99
Tentative: 	243
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Confirmed: 	101
Tentative: 	233
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BorutaPy finished running.

Iteration: 	50 / 50
Confirmed: 	104
Tentative: 	43
Rejected: 	1814

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-359-4a918da9783a> in <module>
     12 
     13 # call transform() on X to filter it down to selected features
---> 14 X_filtered = feat_selector.transform(ENRtrain_featuresT)

~/miniconda3/lib/python3.9/site-packages/boruta/boruta_py.py in transform(self, X, weak)
    220         """
    221 
--> 222         return self._transform(X, weak)
    223 
    224     def fit_transform(self, X, y, weak=False):

~/miniconda3/lib/python3.9/site-packages/boruta/boruta_py.py in _transform(self, X, weak)
    366             X = X[:, self.support_ + self.support_weak_]
    367         else:
--> 368             X = X[:, self.support_]
    369         return X
    370 

~/miniconda3/lib/python3.9/site-packages/pandas/core/frame.py in __getitem__(self, key)
   3022             if self.columns.nlevels > 1:
   3023                 return self._getitem_multilevel(key)
-> 3024             indexer = self.columns.get_loc(key)
   3025             if is_integer(indexer):
   3026                 indexer = [indexer]

~/miniconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   3078             casted_key = self._maybe_cast_indexer(key)
   3079             try:
-> 3080                 return self._engine.get_loc(casted_key)
   3081             except KeyError as err:
   3082                 raise KeyError(key) from err

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

TypeError: '(slice(None, None, None), array([ True,  True,  True, ..., False, False, False]))' is an invalid key


feature_ranks101 = list(zip(top_varaibles, 
                         feat_mine.ranking_, 
                         feat_mine.support_))

# iterate through and print out the results
for feat in feature_ranks101:
    print('Feature: {:<25} Rank: {},  Keep: {}'.format(feat[0], feat[1], feat[2]))

Feature: 1:1368272:G:C             Rank: 1,  Keep: True
Feature: 1:820687:G:C              Rank: 1,  Keep: True
Feature: 1:1640625:C:T             Rank: 1,  Keep: True
Feature: 1:35874:C:G               Rank: 1,  Keep: True
Feature: 1:820684:A:T              Rank: 1,  Keep: True
Feature: 1:826572:G:C              Rank: 14,  Keep: False
Feature: 1:1442606:A:T             Rank: 1,  Keep: True
Feature: 1:114607:C:T              Rank: 1,  Keep: True
Feature: 1:1381938:G:T             Rank: 1,  Keep: True
Feature: 1:663811:A:T              Rank: 1,  Keep: True
Feature: 1:1440950:T:A             Rank: 1,  Keep: True
Feature: 1:663819:C:A              Rank: 1,  Keep: True
Feature: 1:35882:C:G               Rank: 1,  Keep: True
Feature: 1:1614526:T:C             Rank: 1,  Keep: True
Feature: 1:1429732:G:A             Rank: 1,  Keep: True
Feature: 1:35842:T:C               Rank: 1,  Keep: True
Feature: 1:162083:T:C              Rank: 1,  Keep: True
Feature: 1:271616:A:G              Rank: 1,  Keep: True
Feature: 1:83521:A:T               Rank: 1,  Keep: True
Feature: 1:533518:T:C              Rank: 1,  Keep: True
Feature: 1:1341037:A:G             Rank: 1,  Keep: True
Feature: 1:1565560:A:C             Rank: 2,  Keep: False
Feature: 1:1117923:A:C             Rank: 1,  Keep: True
Feature: 1:314088:G:A              Rank: 1,  Keep: True
Feature: 1:403506:G:A              Rank: 462,  Keep: False
Feature: 1:1037926:C:T             Rank: 1,  Keep: True
Feature: 1:1440674:C:T             Rank: 1,  Keep: True
Feature: 1:1105428:G:C             Rank: 1,  Keep: True
Feature: 1:1189640:T:G             Rank: 109,  Keep: False
Feature: 1:790698:G:A              Rank: 208,  Keep: False
Feature: 1:1341042:C:A             Rank: 1,  Keep: True
Feature: 1:866813:T:G              Rank: 2,  Keep: False
Feature: 1:130400:A:G              Rank: 290,  Keep: False
Feature: 1:1184082:A:G             Rank: 25,  Keep: False
Feature: 1:1252490:A:C             Rank: 1,  Keep: True
Feature: 1:1505318:A:G             Rank: 72,  Keep: False
Feature: 1:701381:T:C              Rank: 158,  Keep: False
Feature: 1:1652350:G:T             Rank: 194,  Keep: False
Feature: 1:174141:A:G              Rank: 18,  Keep: False
Feature: 1:1309432:A:T             Rank: 491,  Keep: False
Feature: 1:933368:T:C              Rank: 573,  Keep: False
Feature: 1:1052880:C:T             Rank: 174,  Keep: False
Feature: 1:1056095:C:T             Rank: 2,  Keep: False
Feature: 1:855237:A:C              Rank: 37,  Keep: False
Feature: 1:1135561:A:C             Rank: 1,  Keep: True
Feature: 1:569686:C:T              Rank: 2,  Keep: False
Feature: 1:437166:G:T              Rank: 1,  Keep: True
Feature: 1:966330:C:A              Rank: 588,  Keep: False
Feature: 1:970571:A:T              Rank: 269,  Keep: False
Feature: 1:1353672:C:T             Rank: 1,  Keep: True
Feature: 1:967936:G:T              Rank: 186,  Keep: False
Feature: 1:376822:T:G              Rank: 1,  Keep: True
Feature: 1:753562:T:C              Rank: 1,  Keep: True
Feature: 1:9760:A:C                Rank: 2,  Keep: False
Feature: 1:1566663:G:T             Rank: 1,  Keep: True
Feature: 1:1622936:C:G             Rank: 665,  Keep: False
Feature: 1:1565960:T:C             Rank: 1,  Keep: True
Feature: 1:1105976:A:T             Rank: 1,  Keep: True
Feature: 1:1122412:A:C             Rank: 143,  Keep: False
Feature: 1:1473273:G:T             Rank: 29,  Keep: False
Feature: 1:190816:G:A              Rank: 59,  Keep: False
Feature: 1:310082:G:A              Rank: 1,  Keep: True
Feature: 1:114758:C:A              Rank: 228,  Keep: False
Feature: 1:1390197:G:A             Rank: 521,  Keep: False
Feature: 1:1118203:T:G             Rank: 1,  Keep: True
Feature: 1:565134:G:A              Rank: 3,  Keep: False
Feature: 1:598414:G:C              Rank: 188,  Keep: False
Feature: 1:724693:T:C              Rank: 2,  Keep: False
Feature: 1:1527960:T:A             Rank: 144,  Keep: False
Feature: 1:343859:A:C              Rank: 238,  Keep: False
Feature: 1:772202:C:A              Rank: 2,  Keep: False
Feature: 1:391598:C:T              Rank: 328,  Keep: False
Feature: 1:1278589:G:C             Rank: 116,  Keep: False
Feature: 1:1264011:T:A             Rank: 29,  Keep: False
Feature: 1:1096257:A:T             Rank: 352,  Keep: False
Feature: 1:19068:C:T               Rank: 100,  Keep: False
Feature: 1:1569708:T:A             Rank: 1,  Keep: True
Feature: 1:266339:G:A              Rank: 1,  Keep: True
Feature: 1:693412:G:C              Rank: 67,  Keep: False
Feature: 1:67130:A:G               Rank: 212,  Keep: False
Feature: 1:1193209:G:A             Rank: 306,  Keep: False
Feature: 1:853786:T:A              Rank: 1087,  Keep: False
Feature: 1:701439:G:T              Rank: 169,  Keep: False
Feature: 1:618491:T:C              Rank: 92,  Keep: False
Feature: 1:820901:G:C              Rank: 1,  Keep: True
Feature: 1:1264010:G:A             Rank: 82,  Keep: False
Feature: 1:796014:C:T              Rank: 2,  Keep: False
Feature: 1:1144896:C:G             Rank: 204,  Keep: False
Feature: 1:209769:A:G              Rank: 1196,  Keep: False
Feature: 1:282727:G:C              Rank: 1034,  Keep: False
Feature: 1:1099351:G:A             Rank: 1,  Keep: True
Feature: 1:400087:G:C              Rank: 67,  Keep: False
Feature: 1:229015:A:G              Rank: 1888,  Keep: False
Feature: 1:410819:G:C              Rank: 137,  Keep: False
Feature: 1:438657:T:G              Rank: 152,  Keep: False
Feature: 1:271613:A:C              Rank: 1,  Keep: True
Feature: 1:796051:G:T              Rank: 64,  Keep: False
Feature: 1:1018112:T:A             Rank: 1,  Keep: True
Feature: 1:1591089:C:A             Rank: 723,  Keep: False
Feature: 1:1486353:T:G             Rank: 236,  Keep: False
Feature: 1:999021:T:A              Rank: 369,  Keep: False
Feature: 1:1407306:T:G             Rank: 479,  Keep: False
Feature: 1:541309:A:C              Rank: 573,  Keep: False
Feature: 1:818704:G:T              Rank: 129,  Keep: False
Feature: 1:1139797:G:C             Rank: 467,  Keep: False
Feature: 1:792515:T:G              Rank: 1,  Keep: True
Feature: 1:660357:G:C              Rank: 1,  Keep: True
Feature: 1:330521:A:C              Rank: 29,  Keep: False
Feature: 1:623817:A:G              Rank: 1,  Keep: True
Feature: 1:1535360:A:G             Rank: 497,  Keep: False
Feature: 1:1352381:A:T             Rank: 230,  Keep: False
Feature: 1:1447079:C:A             Rank: 1473,  Keep: False
Feature: 1:266854:G:C              Rank: 621,  Keep: False
Feature: 1:1426744:G:T             Rank: 477,  Keep: False
Feature: 1:1647108:A:G             Rank: 442,  Keep: False
Feature: 1:303876:T:G              Rank: 119,  Keep: False
Feature: 1:784921:A:G              Rank: 1,  Keep: True
Feature: 1:483793:A:G              Rank: 118,  Keep: False
Feature: 1:425307:T:C              Rank: 522,  Keep: False
Feature: 1:194736:T:C              Rank: 1,  Keep: True
Feature: 1:274099:C:T              Rank: 1,  Keep: True
Feature: 1:499832:G:A              Rank: 18,  Keep: False
Feature: 1:351852:A:T              Rank: 153,  Keep: False
Feature: 1:324838:C:T              Rank: 470,  Keep: False
Feature: 1:994056:T:A              Rank: 25,  Keep: False
Feature: 1:1596429:T:C             Rank: 200,  Keep: False
Feature: 1:953729:C:G              Rank: 402,  Keep: False
Feature: 1:1567395:T:A             Rank: 410,  Keep: False
Feature: 1:1451445:G:A             Rank: 492,  Keep: False
Feature: 1:664215:A:G              Rank: 252,  Keep: False
Feature: 1:1228797:C:G             Rank: 334,  Keep: False
Feature: 1:338414:T:C              Rank: 566,  Keep: False
Feature: 1:1468438:T:G             Rank: 2,  Keep: False
Feature: 1:335784:C:T              Rank: 612,  Keep: False
Feature: 1:83523:T:C               Rank: 279,  Keep: False
Feature: 1:802176:G:T              Rank: 1,  Keep: True
Feature: 1:862486:A:G              Rank: 673,  Keep: False
Feature: 1:801948:C:T              Rank: 1,  Keep: True
Feature: 1:562257:C:G              Rank: 32,  Keep: False
Feature: 1:1204974:C:G             Rank: 300,  Keep: False
Feature: 1:871894:A:T              Rank: 670,  Keep: False
Feature: 1:445620:C:G              Rank: 810,  Keep: False
Feature: 1:889668:C:A              Rank: 1,  Keep: True
Feature: 1:1445408:C:A             Rank: 1,  Keep: True
Feature: 1:990380:T:C              Rank: 1,  Keep: True
Feature: 1:784913:G:T              Rank: 526,  Keep: False
Feature: 1:543436:C:T              Rank: 1,  Keep: True
Feature: 1:660193:A:T              Rank: 906,  Keep: False
Feature: 1:80354:G:C               Rank: 545,  Keep: False
Feature: 1:1169194:G:T             Rank: 553,  Keep: False
Feature: 1:114667:G:A              Rank: 34,  Keep: False
Feature: 1:1240451:C:T             Rank: 1211,  Keep: False
Feature: 1:448267:A:G              Rank: 1,  Keep: True
Feature: 1:1040052:T:C             Rank: 209,  Keep: False
Feature: 1:1165929:C:T             Rank: 554,  Keep: False
Feature: 1:319203:T:A              Rank: 1327,  Keep: False
Feature: 1:550757:G:C              Rank: 377,  Keep: False
Feature: 1:266343:A:G              Rank: 6,  Keep: False
Feature: 1:1125741:G:C             Rank: 2,  Keep: False
Feature: 1:1526154:C:T             Rank: 109,  Keep: False
Feature: 1:426520:C:A              Rank: 188,  Keep: False
Feature: 1:1629796:T:A             Rank: 1,  Keep: True
Feature: 1:285572:C:A              Rank: 496,  Keep: False
Feature: 1:541275:G:C              Rank: 1,  Keep: True
Feature: 1:994398:G:T              Rank: 223,  Keep: False
Feature: 1:1279560:T:C             Rank: 21,  Keep: False
Feature: 1:964957:G:A              Rank: 1,  Keep: True
Feature: 1:450818:A:C              Rank: 981,  Keep: False
Feature: 1:84592:G:C               Rank: 490,  Keep: False
Feature: 1:554351:T:C              Rank: 4,  Keep: False
Feature: 1:1352494:A:G             Rank: 202,  Keep: False
Feature: 1:899304:G:C              Rank: 1,  Keep: True
Feature: 1:1196460:G:A             Rank: 1,  Keep: True
Feature: 1:820321:G:T              Rank: 5,  Keep: False
Feature: 1:289012:A:C              Rank: 339,  Keep: False
Feature: 1:1418427:A:G             Rank: 615,  Keep: False
Feature: 1:1222563:G:C             Rank: 62,  Keep: False
Feature: 1:1279942:C:T             Rank: 494,  Keep: False
Feature: 1:1313398:G:A             Rank: 424,  Keep: False
Feature: 1:1472154:C:A             Rank: 525,  Keep: False
Feature: 1:961005:T:C              Rank: 551,  Keep: False
Feature: 1:1566885:C:A             Rank: 628,  Keep: False
Feature: 1:566802:G:A              Rank: 898,  Keep: False
Feature: 1:1185187:G:T             Rank: 1,  Keep: True
Feature: 1:754710:A:C              Rank: 137,  Keep: False
Feature: 1:48877:C:G               Rank: 2,  Keep: False
Feature: 1:661016:T:A              Rank: 1,  Keep: True
Feature: 1:323915:C:G              Rank: 527,  Keep: False
Feature: 1:49385:C:G               Rank: 104,  Keep: False
Feature: 1:1288996:C:T             Rank: 978,  Keep: False
Feature: 1:413173:T:G              Rank: 1,  Keep: True
Feature: 1:776928:T:A              Rank: 652,  Keep: False
Feature: 1:1100627:G:T             Rank: 889,  Keep: False
Feature: 1:1297275:A:C             Rank: 386,  Keep: False
Feature: 1:1587053:C:G             Rank: 1,  Keep: True
Feature: 1:210343:C:G              Rank: 397,  Keep: False
Feature: 1:872929:G:C              Rank: 1187,  Keep: False
Feature: 1:1614105:T:C             Rank: 793,  Keep: False
Feature: 1:623565:A:C              Rank: 371,  Keep: False
Feature: 1:502343:G:T              Rank: 341,  Keep: False
Feature: 1:416562:A:C              Rank: 2,  Keep: False
Feature: 1:71336:C:G               Rank: 1933,  Keep: False
Feature: 1:948703:G:T              Rank: 192,  Keep: False
Feature: 1:880815:T:G              Rank: 1449,  Keep: False
Feature: 1:721781:A:T              Rank: 121,  Keep: False
Feature: 1:85489:T:G               Rank: 1331,  Keep: False
Feature: 1:1189979:G:T             Rank: 172,  Keep: False
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Feature: 1:1211029:C:T             Rank: 1925,  Keep: False
Feature: 1:994381:G:T              Rank: 226,  Keep: False
Feature: 1:1629065:C:A             Rank: 1110,  Keep: False
Feature: 1:710045:A:C              Rank: 1362,  Keep: False
Feature: 1:1472836:T:G             Rank: 910,  Keep: False
Feature: 1:1556476:G:A             Rank: 1117,  Keep: False
Feature: 1:111649:G:C              Rank: 1046,  Keep: False
Feature: 1:283774:G:C              Rank: 1103,  Keep: False
Feature: 1:317613:A:C              Rank: 1683,  Keep: False
Feature: 1:667258:A:T              Rank: 930,  Keep: False
Feature: 1:1046555:T:G             Rank: 1923,  Keep: False
Feature: 1:1165260:A:G             Rank: 995,  Keep: False
Feature: 1:1110189:C:A             Rank: 1209,  Keep: False
Feature: 1:1417235:A:G             Rank: 650,  Keep: False
Feature: 1:1102521:T:A             Rank: 1813,  Keep: False
Feature: 1:253076:G:A              Rank: 1782,  Keep: False
Feature: 1:1344774:A:G             Rank: 1505,  Keep: False
Feature: 1:538293:G:A              Rank: 1048,  Keep: False
Feature: 1:1092546:G:A             Rank: 1980,  Keep: False
Feature: 1:309502:T:A              Rank: 1916,  Keep: False
Feature: 1:1138717:G:C             Rank: 771,  Keep: False
Feature: 1:1277309:T:C             Rank: 1331,  Keep: False
Feature: 1:795453:C:T              Rank: 1408,  Keep: False
Feature: 1:1185736:C:T             Rank: 929,  Keep: False
Feature: 1:257704:C:T              Rank: 1638,  Keep: False
Feature: 1:1240985:T:G             Rank: 702,  Keep: False
Feature: 1:1548384:C:G             Rank: 992,  Keep: False
Feature: 1:770751:A:C              Rank: 258,  Keep: False
Feature: 1:162980:T:G              Rank: 1872,  Keep: False
Feature: 1:559236:A:G              Rank: 1020,  Keep: False
Feature: 1:849440:C:A              Rank: 935,  Keep: False
Feature: 1:1227138:A:T             Rank: 1314,  Keep: False
Feature: 1:1252462:A:G             Rank: 495,  Keep: False
Feature: 1:794768:C:A              Rank: 645,  Keep: False
Feature: 1:22490:A:T               Rank: 1431,  Keep: False
Feature: 1:194509:C:T              Rank: 1156,  Keep: False
Feature: 1:1220355:T:A             Rank: 1282,  Keep: False
Feature: 1:493389:A:T              Rank: 1697,  Keep: False
Feature: 1:921779:G:A              Rank: 1724,  Keep: False
Feature: 1:89623:G:T               Rank: 96,  Keep: False
Feature: 1:864591:G:T              Rank: 273,  Keep: False
Feature: 1:291565:C:G              Rank: 1314,  Keep: False
Feature: 1:834378:C:G              Rank: 360,  Keep: False
Feature: 1:1308005:A:G             Rank: 1417,  Keep: False
Feature: 1:458604:T:C              Rank: 316,  Keep: False
Feature: 1:1115617:A:G             Rank: 1943,  Keep: False
Feature: 1:978928:T:A              Rank: 10,  Keep: False
Feature: 1:1262152:G:C             Rank: 596,  Keep: False
Feature: 1:1144532:C:A             Rank: 423,  Keep: False
Feature: 1:245422:A:T              Rank: 1279,  Keep: False
Feature: 1:748327:A:T              Rank: 1483,  Keep: False
Feature: 1:1555888:T:G             Rank: 1542,  Keep: False
Feature: 1:813428:A:G              Rank: 1832,  Keep: False
Feature: 1:1151794:G:A             Rank: 1644,  Keep: False
Feature: 1:1036993:T:A             Rank: 823,  Keep: False
Feature: 1:347150:A:T              Rank: 1233,  Keep: False
Feature: 1:850345:A:C              Rank: 1748,  Keep: False
Feature: 1:213220:G:T              Rank: 1085,  Keep: False
Feature: 1:737529:C:G              Rank: 1546,  Keep: False
Feature: 1:464525:A:G              Rank: 1794,  Keep: False
Feature: 1:76560:T:A               Rank: 1415,  Keep: False
Feature: 1:1084592:A:C             Rank: 839,  Keep: False
Feature: 1:211454:G:A              Rank: 963,  Keep: False
Feature: 1:860089:C:A              Rank: 1697,  Keep: False
Feature: 1:987069:C:T              Rank: 773,  Keep: False
Feature: 1:460846:T:G              Rank: 1446,  Keep: False
Feature: 1:1603911:C:G             Rank: 1784,  Keep: False
Feature: 1:1544748:T:C             Rank: 1222,  Keep: False
Feature: 1:424559:A:T              Rank: 838,  Keep: False
Feature: 1:1110851:T:A             Rank: 457,  Keep: False
Feature: 1:388537:A:G              Rank: 581,  Keep: False
Feature: 1:413509:T:C              Rank: 1830,  Keep: False
Feature: 1:63120:G:A               Rank: 1039,  Keep: False
Feature: 1:1821:C:A                Rank: 1533,  Keep: False
Feature: 1:1477843:C:A             Rank: 1031,  Keep: False
Feature: 1:1335085:A:C             Rank: 1908,  Keep: False
Feature: 1:278703:A:G              Rank: 1344,  Keep: False
Feature: 1:1030944:C:G             Rank: 1987,  Keep: False
Feature: 1:629398:C:T              Rank: 1216,  Keep: False
Feature: 1:83536:G:A               Rank: 20,  Keep: False
Feature: 1:563035:T:C              Rank: 687,  Keep: False
Feature: 1:1500754:A:G             Rank: 233,  Keep: False
Feature: 1:109237:A:G              Rank: 1871,  Keep: False
Feature: 1:303115:A:C              Rank: 397,  Keep: False
Feature: 1:586151:T:C              Rank: 1966,  Keep: False
Feature: 1:432263:T:C              Rank: 1569,  Keep: False
Feature: 1:498146:G:A              Rank: 1002,  Keep: False
Feature: 1:281173:T:G              Rank: 676,  Keep: False
Feature: 1:919328:T:G              Rank: 1662,  Keep: False
Feature: 1:1509022:G:A             Rank: 454,  Keep: False
Feature: 1:86716:A:T               Rank: 1413,  Keep: False
Feature: 1:1263385:A:G             Rank: 416,  Keep: False
Feature: 1:209599:T:A              Rank: 952,  Keep: False
Feature: 1:927712:T:G              Rank: 308,  Keep: False
Feature: 1:649301:T:A              Rank: 1247,  Keep: False
Feature: 1:1180104:G:A             Rank: 939,  Keep: False
Feature: 1:1190541:A:T             Rank: 1322,  Keep: False
Feature: 1:640739:C:A              Rank: 558,  Keep: False
Feature: 1:129196:C:G              Rank: 1740,  Keep: False
Feature: 1:960157:T:C              Rank: 1094,  Keep: False
Feature: 1:115005:G:T              Rank: 675,  Keep: False
Feature: 1:744963:T:G              Rank: 1664,  Keep: False
Feature: 1:325224:A:G              Rank: 1800,  Keep: False
Feature: 1:1434423:C:T             Rank: 803,  Keep: False
Feature: 1:220273:A:T              Rank: 1595,  Keep: False


for i in feature_ranks:
    if i[0]=="1:2138:C:A":
        print(i)


1:2138:C:A	A	0.2925	2.30259
1:6076:T:A	A	0.18125	2.70805
1:147303:G:T	T	0.38375	1.94591
1:153874:G:A	A	0.18625	2.30259
1:204902:C:G	G	0.16875	1.09861
1:221430:A:G	G	0.11375	2.3979
1:272237:G:T	T	0.13875	0.693147
1:448267:A:G	G	0.35375	1.38629
1:456736:G:C	C	0.2325	2.99573
1:576589:A:C	C	0.18	1.38629
1:663819:C:A	A	0.1675	2.70805
1:701020:T:C	C	0.15375	0.693147
1:790484:A:T	T	0.3825	2.99573
1:952541:C:G	G	0.3525	2.3979
1:1150474:A:G	G	0.205	1.94591
1:1533881:T:C	C	0.35375	1.09861


feature_impDF['Features'][feature_impDF["Features"]=="1:2138:C:A"]

196461    1:2138:C:A
Name: Features, dtype: object


196461    1:2138:C:A
Name: Features, dtype: object


feature_impDF[feature_impDF["Features"]=="1:147303:G:T"]


feature_impDF[feature_impDF["Features"]=="1:6076:T:A"]


        Features	Importance
15598	1:6076:T:A	0.000019


feature_impDF[feature_impDF["Features"]=="1:1533881:T:C"]


        Features	Importance
83864	1:1533881:T:C	0.0

Imports¶

Hypterparameter tuning¶

1. Gini Vs Entropy¶

2. n_estimators¶

3. Depth of the tree¶

4. Maximum features¶

5. Minimum samples leaf¶

Initial Model and Variable selection¶

Distribution of Variable Importance Measure values for all features¶

Understanding Model Accuracy with respect to VIM or number of features¶

Plot 1¶

Plot2¶

Correlation Among Selected Features¶

Correlation Among Selected Features and Feature Clustering¶

VIM¶

Boruta¶

Data Wrangling for final list of features with meta data¶

SHAP¶

For shap clustering¶

Class Imbalance¶

BacGWASim Modeling¶

Boruta to BACGWASIM¶

VIM for bacGWASim¶

	assembly_id	Uberstrain	Name	GWAS group
0	2003-103	NaN	2003-103	Progressive towards Cancer
1	2004-20	NaN	2004-20	Progressive towards Cancer
2	2005-100	NaN	2005-100	Non Atrophic Gastritis
3	2005-126	NaN	2005-126	Progressive towards Cancer
4	2005-98	NaN	2005-98	Non Atrophic Gastritis
...	...	...	...	...
861	ms2	NaN	ms2	Non Atrophic Gastritis
862	ms203	NaN	ms203	Non Atrophic Gastritis
863	ms23	NaN	ms23	Non Atrophic Gastritis
864	ms931	NaN	ms931	Gastric Cancer
865	ms965	NaN	ms965	Gastric Cancer

	0	1	2	3	4	5	6	7	8	9	...	866	867	868	869	870	871	872	873	874	POSITION
3	CP001217	26023	.	G	A	.	PASS	TYPE=snp	GT	0	...	0	0	0	0	0	0	0	0	0	26023.G/A
4	CP001217	26024	.	T	C	.	PASS	TYPE=snp	GT	0	...	0	0	0	0	0	0	0	0	0	26024.T/C
15	CP001217	26063	.	C	T	.	PASS	TYPE=snp	GT	0	...	0	0	0	0	0	0	0	0	0	26063.C/T
19	CP001217	26074	.	T	G	.	PASS	TYPE=snp	GT	0	...	0	0	0	0	0	0	0	0	0	26074.T/G
21	CP001217	26079	.	T	C	.	PASS	TYPE=snp	GT	0	...	0	0	0	0	0	0	0	0	0	26079.T/C
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
70594	CP001217	1484935	.	G	A	.	PASS	TYPE=snp	GT	0	...	0	0	0	0	0	0	0	0	0	1484935.G/A
70595	CP001217	1484936	.	C	A,T	.	PASS	TYPE=snp	GT	0	...	0	0	0	0	0	0	0	0	0	1484936.C/A,T
70597	CP001217	1484938	.	T	C	.	PASS	TYPE=snp	GT	0	...	0	0	0	0	0	0	0	0	0	1484938.T/C
70598	CP001217	1484942	.	C	T	.	PASS	TYPE=snp	GT	0	...	0	0	0	0	0	0	0	0	0	1484942.C/T
70599	CP001217	1484944	.	A	G	.	PASS	TYPE=snp	GT	0	...	0	0	0	0	0	0	0	0	0	1484944.A/G

866	26023.G/A	26024.T/C	26063.C/T	26074.T/G	26079.T/C	26080.T/C	26101.T/C	26104.G/A	26107.G/A	26110.A/G	...	1484914.G/A	1484915.T/C	1484918.C/T	1484927.T/C	1484930.G/T	1484935.G/A	1484936.C/A,T	1484938.T/C	1484942.C/T	1484944.A/G
0	0	0	0	0	0	0	0	0	0	0	...	0	0	1	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
861	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
862	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
863	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
864	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
865	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

866	26023.G/A	26024.T/C	26063.C/T	26074.T/G	26079.T/C	26080.T/C	26101.T/C	26104.G/A	26107.G/A	26110.A/G	...	1484914.G/A	1484915.T/C	1484918.C/T	1484927.T/C	1484930.G/T	1484935.G/A	1484936.C/A,T	1484938.T/C	1484942.C/T	1484944.A/G
0	0	0	0	0	0	0	0	0	0	0	...	0	0	1	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
861	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
862	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
863	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
864	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
865	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

	Features	Importance
0	221331.T/C	0.002591
1	962858.G/A	0.001864
2	1115013.G/A,T	0.001660
3	712033.A/C,G,T	0.001596
4	908373.T/C	0.001425
...	...	...
29586	828682.C/A	0.000000
29587	828668.A/G	0.000000
29588	828666.T/C	0.000000
29589	828662.G/A	0.000000
29590	1484944.A/G	0.000000

	0	1	2	3	4	5	6	7	8	gene_name
0	CP001217.1	Genbank	gene	13	429	.	-	.	gene_id "HPP12_0001"; gbkey "Gene"; gene "nusB...	nusB
8	CP001217.1	Genbank	gene	911	1741	.	-	.	gene_id "HPP12_0003"; gbkey "Gene"; gene "kdsA...	kdsA
16	CP001217.1	Genbank	gene	2515	3198	.	+	.	gene_id "HPP12_0005"; gbkey "Gene"; gene "pyrF...	pyrF
20	CP001217.1	Genbank	gene	3199	4029	.	+	.	gene_id "HPP12_0006"; gbkey "Gene"; gene "panC...	panC
24	CP001217.1	Genbank	gene	4043	4118	.	-	.	gene_id "HPP12_t01"; gbkey "Gene"; gene_biotyp...	tRNA
...	...	...	...	...	...	...	...	...	...	...
6308	CP001217.1	Genbank	gene	1662090	1662362	.	+	.	gene_id "HPP12_1566"; gbkey "Gene"; gene "flhB...	flhB-2
6336	CP001217.1	Genbank	gene	1667383	1668171	.	+	.	gene_id "HPP12_1573"; gbkey "Gene"; gene "pdxJ...	pdxJ
6340	CP001217.1	Genbank	gene	1668173	1669096	.	+	.	gene_id "HPP12_1574"; gbkey "Gene"; gene "pdxA...	pdxA
6344	CP001217.1	Genbank	gene	1669161	1670183	.	+	.	gene_id "HPP12_1575"; gbkey "Gene"; gene "ydiE...	ydiE
6348	CP001217.1	Genbank	gene	1670362	1671150	.	+	.	gene_id "HPP12_1576"; gbkey "Gene"; gene "flgG...	flgG-2

	0	1	2
0	221331.T/C	221331.0	T/C
1	962858.G/A	962858.0	G/A
2	1115013.G/A,T	1115013.0	G/A,T
3	712033.A/C,G,T	712033.0	A/C,G,T
4	908373.T/C	908373.0	T/C
...	...	...	...
137	1027216.C/T	1027216.0	C/T
138	1293845.T/C	1293845.0	T/C
139	1341574.C/T	1341574.0	C/T
140	676016.G/A	676016.0	G/A
141	314047.G/A,C	314047.0	G/A,C

	Features	Importance
0	712033.A/C,G,T	0.018980
1	221331.T/C	0.018135
2	310663.A/G,T	0.015052
3	828717.T/A,C,G	0.014518
4	908373.T/C	0.013676
5	1115013.G/A,T	0.013079
6	1181754.C/A,T	0.012954
7	962858.G/A	0.012850
8	1131910.G/A,C,T	0.012523
9	1009179.C/A,T	0.012106

	Phenotype	CDS Start	CDS End	Gene ID	Product	gene
0	221331.T/C	220874	221671	"HPP12_0216"	"cdp-diacylglycerol synthetase"	"cdsA"
1	962858.G/A	962720	964273	"HPP12_0910"	"outer membrane protein HopC/AlpA"	"alpA"
2	1115013.G/A,T	1114613	1115281	"HPP12_1044"	"thiol:disulfide interchange protein"	NaN
3	712033.A/C,G,T	709540	712278	"HPP12_0668"	"protective surface antigen D15"	NaN
4	908373.T/C	908318	909700	"HPP12_0858"	"ADP-heptose synthase"	"waaE"
...	...	...	...	...	...	...
131	1027216.C/T	1026881	1027957	"HPP12_0966"	"cobalt-zinc-cadmium resistance protein"	NaN
132	1293845.T/C	1293546	1296086	"HPP12_1207"	"alanyl-tRNA synthetase"	"alaS"
133	1341574.C/T	1341103	1341837	"HPP12_1251"	"acid phosphatase lipoprotein"	NaN
134	676016.G/A	673942	676191	"HPP12_0634"	"DNA mismatch repair protein"	"mutS"
135	314047.G/A,C	312634	314283	"HPP12_0297"	"periplasmic dipeptide-binding protein"	"dppA"

	Top 10 SHAP Features for detecting Gastric Cancer	feature_importance_vals
0	221331.T/C	0.009661
3	712033.A/C,G,T	0.008822
24	119199.C/T	0.007481
88	884241.G/A	0.006717
74	34261.A/G	0.005263
11	332096.C/T	0.005236
4	908373.T/C	0.004803
32	819317.G/A,C	0.004542
91	815966.A/C,T	0.004207
58	1320905.T/A,G	0.004160

	col_name	feature_importance_vals
0	221331.T/C	0.023717
4	908373.T/C	0.016667
19	1009179.C/A,T	0.013820
5	1181754.C/A,T	0.013696
15	828717.T/A,C,G	0.013456
2	1115013.G/A,T	0.012920
1	962858.G/A	0.012815
6	310663.A/G,T	0.011992
37	1140792.G/A	0.011229
3	712033.A/C,G,T	0.011161
18	147200.T/C	0.010158
13	178004.T/C	0.009013
21	661042.G/A	0.009009
51	1026812.C/T	0.008813
28	1010062.G/A,C,T	0.008680
58	1320905.T/A,G	0.008528
44	934087.C/T	0.008107
55	343320.C/T	0.008032
87	1278445.T/C	0.008013
34	290613.A/G	0.007652

	col_name	feature_importance_vals
1	962858.G/A	0.016734
0	221331.T/C	0.014483
4	908373.T/C	0.013257
5	1181754.C/A,T	0.012500
6	310663.A/G,T	0.012483
15	828717.T/A,C,G	0.012052
19	1009179.C/A,T	0.011937
2	1115013.G/A,T	0.010491
37	1140792.G/A	0.009369
79	1093801.A/G	0.009096
18	147200.T/C	0.008737
51	1026812.C/T	0.008677
87	1278445.T/C	0.008573
44	934087.C/T	0.007827
76	922177.G/A	0.007365
67	1131910.G/A,C,T	0.007188
42	1365456.C/A,T	0.007106
48	1325191.G/A	0.007092
16	216718.C/T	0.006898
55	343320.C/T	0.006801

866	26023.G/A	26024.T/C	26063.C/T	26074.T/G	26079.T/C	26080.T/C	26101.T/C	26104.G/A	26107.G/A	26110.A/G	...	1484914.G/A	1484915.T/C	1484918.C/T	1484927.T/C	1484930.G/T	1484935.G/A	1484936.C/A,T	1484938.T/C	1484942.C/T	1484944.A/G
0	0	0	0	0	0	0	0	0	0	0	...	0	0	1	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
861	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
862	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
863	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
864	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
865	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

866	26023.G/A	26024.T/C	26063.C/T	26074.T/G	26079.T/C	26080.T/C	26101.T/C	26104.G/A	26107.G/A	26110.A/G	...	1484914.G/A	1484915.T/C	1484918.C/T	1484927.T/C	1484930.G/T	1484935.G/A	1484936.C/A,T	1484938.T/C	1484942.C/T	1484944.A/G
0	0	0	0	0	0	0	0	0	0	0	...	0	0	1	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
861	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
862	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
863	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
864	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
865	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

ID	1:9:C:A	1:10:G:T	1:16:C:T	1:26:A:T	1:31:A:G	1:32:G:C	1:33:C:T	1:38:C:G	1:39:C:T	1:51:C:G	...	1:1673778:G:T	1:1673782:C:T	1:1673783:C:A	1:1673790:T:A	1:1673791:G:C	1:1673793:C:A	1:1673796:G:A	1:1673799:G:C	1:1673801:A:G	1:1673810:A:C
zero	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	0	0	0	1	1	0	0	0	0	0	...	0	0	1	0	1	1	0	0	1	1
2	1	0	0	0	0	0	0	0	0	1	...	0	0	0	0	0	0	0	0	0	1
3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	1	0	0	1	1
4	1	0	1	0	0	0	0	0	0	1	...	0	0	1	0	1	1	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
795	1	0	1	0	0	0	0	0	0	0	...	0	0	1	0	1	1	0	0	1	1
796	1	0	1	0	0	0	0	0	0	0	...	0	0	1	0	1	1	0	0	1	1
797	1	0	1	1	1	1	0	0	1	1	...	1	1	1	0	0	1	0	0	1	0
798	1	0	1	0	0	0	0	0	0	1	...	1	1	1	0	0	1	0	0	1	0
799	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

	Features	Importance
0	1:1368272:G:C	0.000914
1	1:820687:G:C	0.000721
2	1:1640625:C:T	0.000620
3	1:35874:C:G	0.000585
4	1:820684:A:T	0.000566
...	...	...
287142	1:586477:C:A	0.000000
287143	1:586491:T:A	0.000000
287144	1:586512:G:A	0.000000
287145	1:586521:T:G	0.000000
287146	1:1673810:A:C	0.000000

866	26023.G/A	26024.T/C	26063.C/T	26074.T/G	26079.T/C	26080.T/C	26101.T/C	26104.G/A	26107.G/A	26110.A/G	...	1484914.G/A	1484915.T/C	1484918.C/T	1484927.T/C	1484930.G/T	1484935.G/A	1484936.C/A,T	1484938.T/C	1484942.C/T	1484944.A/G
0	0	0	0	0	0	0	0	0	0	0	...	0	0	1	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
861	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
862	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
863	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
864	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
865	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

866	26023.G/A	26024.T/C	26063.C/T	26074.T/G	26079.T/C	26080.T/C	26101.T/C	26104.G/A	26107.G/A	26110.A/G	...	1484914.G/A	1484915.T/C	1484918.C/T	1484927.T/C	1484930.G/T	1484935.G/A	1484936.C/A,T	1484938.T/C	1484942.C/T	1484944.A/G
0	0	0	0	0	0	0	0	0	0	0	...	0	0	1	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
861	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
862	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
863	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
864	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
865	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0